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Posts Tagged 'ngs'
Making next-generation sequencing analysis pipelines easier with BioCloudCentral and Galaxy integration
My previous post described running an automated exome pipeline using CloudBioLinux and CloudMan, and generated incredibly useful feedback. Comments and e-mails pointed out potential points of confusion for new users deploying the process on custom data. I also had the chance to get hands on with researchers running CloudBioLinux and CloudMan during the AWS Genomics Event (talk slides are available).
The culmination of all this feedback are two new development projects from the CloudBioLinux community, aimed at making it easier to run custom analysis pipelines:
BioCloudCentral — A web service that launches CloudBioLinux and CloudMan clusters on Amazon Web Services hardware. This removes all of the manual steps involved in setting up security groups and launching a CloudBioLinux instance. A user only needs to sign up for an AWS account; BioCloudCentral takes care of everything else.
A custom Galaxy integrated front-end to next-generation sequencing pipelines. A jQuery UI wizard interface manages the intake of sequences and specification of parameters. It runs an automated backend processing pipeline with the structured input data, uploading results into Galaxy data libraries for additional analysis.
Special thanks are due to Enis Afgan for his help building these tools. He provided his boto expertise to the BioCloudCentral Amazon interaction, and generalized CloudMan to support the additional flexibility and automation on display here.
This post describes using these tools to start a CloudMan instance, create an SGE cluster and run a distributed variant calling analysis, all from the browser. The behind the scene details described earlier are available: the piepline uses a CloudBioLinux image containing a wide variety of bioinformatics software and you can use ssh or an NX graphical client to connect to the instance. This is the unique approach behind CloudBioLinux and CloudMan: they provide an open framework for building automated, easy-to-use workflows.
BioCloudCentral — starting a CloudBioLinux instance
To get started, sign up for an Amazon Web services account. This gives you access to on demand computing where you pay per hour of usage. Once signed up, you will need your Access Key ID and Secret Access Key from the Amazon security credentials page.
With these, navigate to BioCloudCentral and fill out the simple entry form. In addition to your access credentials, enter your choice of a name used to identify the cluster, and your choice of password to access the CloudMan web interface and the cluster itself via ssh or NX.
Clicking submit launches a CloudBioLinux server on Amazon. Be careful, since you are now paying per hour for your machine; remember to shut it down when finished.
Before leaving the monitoring page, you want to download a pre-formatted user-data file; this allows you to later start the same CloudMan instance directly from the Amazon web services console.
CloudMan — managing the cluster
The monitoring page on BioCloudCentral provides links directly to the CloudMan web interface. On the welcome page, start a shared CloudMan instance with this identifier:
This shared instance contains the custom Galaxy interface we will use, along with FASTQ sequence files for demonstration purposes. CloudMan will start up the filesystem, SGE, PostgreSQL and Galaxy. Once launched, you can use the CloudMan interface to add additional machines to your cluster for processing.
Galaxy pipeline interface — running the analysis
This Galaxy instance is a fork of the main codebase containing a custom pipeline interface in addition to all of the standard Galaxy tools. It provides an intuitive way to select FASTQ files for processing. Login with the demonstration account (user: email@example.com; password: example) and load FASTQ files along with target and bait BED files into your active history. Then work through the pipeline wizard step by step to start an analysis:
The Galaxy interface builds a configuration file describing the parameters and inputs, and submits this to the backend analysis server. This server kicks off processing, distributing the analysis across the SGE cluster. For the test data, processing will take approximately 4 hours on a cluster with a single additional work node (Large instance type).
Galaxy — retrieving and displaying results
The analysis pipeline uploads the finalized results into Galaxy data libraries. For this demonstration, the example user has results from a previous run in the data library so you don't need to wait for the analysis to finish. This folder contains alignment data in BAM format, coverage information in BigWig format, a VCF file of variant calls, a tab separate file with predicted variant effects, and a PDF file of summary information. After importing these into your active Galaxy history, you can perform additional analysis on the data, including visualization in the UCSC genome browser:
As a reminder, don't forget to terminate your cluster when finished. You can do this either from the CloudMan web interface or the Amazon console.
Analysis pipeline details and extending this work
The backend analysis pipeline is a freely available set of Python modules included on the CloudBioLinux AMI. The pipeline closely follows current best practice variant detection recommendations from the Broad GATK team:
- FASTQ alignment with BWA; source code
- Base quality score recalibration with GATK: source code
- Local realignment around indels with GATK: source code:
- Variant calling (SNPs and indels) using the GATK Unified Genotyper: source code
- Variant effect estimation with snpEff: source code
- Read coverage visualization with wigToBigWig: source code
The pipeline framework design is general, allowing incorporation of alternative aligners or variant calling algorithms.
We hope that in addition to being directly useful, this framework can fit within the work environments of other developers. The flexible toolkit used is: CloudBioLinux with open source bioinformatics libraries, CloudMan with a managed SGE cluster, Galaxy with a custom pipeline interface, and finally Python to parallelize and manage the processing. We invite you to fork and extend any of the different components. Thank you again to everyone for the amazing feedback on the analysis pipeline and CloudBioLinux.
Parallel approaches in next-generation sequencing analysis pipelines
My last post described a distributed exome analysis pipeline implemented on the CloudBioLinux and CloudMan frameworks. This was a practical introduction to running the pipeline on Amazon resources. Here I'll describe how the pipeline runs in parallel, specifically diagramming the workflow to identify points of parallelization during lane and sample processing.
Incredible innovation in throughput makes parallel processing critical for next-generation sequencing analysis. When a single Hi-Seq run can produce 192 samples (2 flowcells x 8 lanes per flowcell x 12 barcodes per lane), the analysis steps quickly become limited by the number of processing cores available.
The heterogeneity of architectures utilized by researchers is a major challenge in building re-usable systems. A pipeline needs to support powerful multi-core servers, clusters and virtual cloud-based machines. The approach we took is to scale at the level of individual samples, lanes and pipelines, exploiting the embarassingly parallel nature of the computation. An AMQP messaging queue allows for communication between processes, independent of the system architecture. This flexible approach allows the pipeline to serve as a general framework that can be easily adjusted or expanded to incorporate new algorithms and analysis methods.
Process overview — points for parallel implementations
The first level of parallelization occurs during processing of each fastq lane. We split the file into individualized barcoded components, followed by alignment and BAM processing. The result is a sorted BAM file for each barcoded sub-sample, given a set of input fastq files:
The pipeline merges samples present in barcodes on multiple lanes, producing a single representative BAM file. The next step parallelizes the processing of each alignment file with read quality assessment, preparation for visualization and variant calling:
The variant calling steps utilize The Genome Analysis Toolkit (GATK) from the Broad Institute. It prepares alignments by recalibrating initial quality scores given the aligned sequences and consistently realigning reads around indels. The Unified Genotyper identifies variants from this prepared alignment file, then uses these variants along with known true sites for assigning quality scores and filtering to a final set of calls:
Subsequent steps include assessment of variant effects using snpEff and haplotype phasing of variants in diploid organism analyses.
Messaging approach to parallel execution
The process diagrams illustrate points of parallel execution for each fastq file and sample analysis. Practically, a top level analysis server manages each of the sub-processes. A command line script, a LIMS system or a specialized Galaxy interface start this top level process. RabbitMQ messaging facilitates communication between the analysis controller and processing nodes:
In my previous post, CloudMan manages this entire process. The web interface controls a pre-configured SGE cluster and a custom script starts the job on this cluster. However, the general nature of the pipeline architecture allows this to work equally well on multiple core machines or a heterogeneous set of connected machines.
The CloudMan work demonstrates that clusters, especially on-demand virtual images like those available from Amazon, are be a powerful way to scale analyses. Equally important, it provides an open platform to share these pipelines and encourage re-use. The code for the pipeline is available from the bcbio-nextgen GitHub repository
Distributed exome analysis pipeline with CloudBioLinux and CloudMan
A major challenge in building analysis pipelines for next-generation sequencing data is combining a large number of processing steps in a flexible, scalable manner. Current best-practice software needs to be installed and configured alongside the custom code to chain individual programs together. Scaling to handle increasing throughput requires running that custom code on a wide variety of parallel architectures, from single multicore machines to heterogeneous clusters.
Establishing community resources that meet the challenges of building these pipelines ensures that bioinformatics programmers can share the burden of building large scale systems. Two open-source efforts which aim at providing this type of architecture are:
CloudBioLinux — A community effort to create shared images filled with bioinformatics software and libraries, using an automated build environment.
CloudMan — Uses CloudBioLinux as a platform to build a full SGE cluster environment. Written by Enis Afgan and the Galaxy Team, CloudMan is used to provide a ready-to-run, dynamically scalable version of Galaxy on Amazon AWS.
Here we combine CloudBioLinux software with a CloudMan SGE cluster to build a fully automated pipeline for processing high throughput exome sequencing data:
- The underlying analysis software is from CloudBioLinux.
- CloudMan provides an SGE cluster managed via a web front end.
- RabbitMQ is used for communication between cluster nodes.
- An automated pipeline, written in Python, organizes parallel processing across the cluster.
Below are instructions for starting a cluster on Amazon EC2 resources to run an exome sequencing pipeline that processes FASTQ sequencing reads, producing fully annotated variant calls.
Start cluster with CloudBioLinux and CloudMan
Start in the Amazon web console, a convenient front end for managing EC2 servers. The first step is to follow the CloudMan setup instructions to create an Amazon account and set up appropriate security groups and user data. The wiki page contains detailed screencasts. Below is a short screencast showing how to boot your CloudBioLinux specific CloudMan server:
Once this is booted, proceed to the CloudMan web interface on the server and startup an instance from this shared identifier:
This screencast shows all of the details, including starting an additional node on the SGE cluster:
Configure AMQP messaging
Edit: The AMQP messaging steps have now been full automated so the configuration steps in this section are no longer required. Skip down to the 'Run Analysis' section to start processing the data immediately.
With your server booted and ready to run, the next step is to configure RabbitMQ messaging to communicate between nodes on your cluster. In the AWS console, find the external and internal hostname of the head machine. Start by opening an ssh connection to the machine with the external hostname:
$ ssh -i your-keypair email@example.com
Edit the
/export/data/galaxy/universe_wsgi.ini configuration file to add the internal hostname. After editing, the AMQP section will look like:
host = ip-10-125-10-182.ec2.internal
port = 5672
userid = biouser
password = tester
Finally, add the user and virtual host to the running RabbitMQ server on the master node with 3 commands:
$ sudo rabbitmqctl add_user biouser tester
creating user "biouser" ...
$ sudo rabbitmqctl add_vhost bionextgen
creating vhost "bionextgen" ...
$ sudo rabbitmqctl set_permissions -p bionextgen biouser ".*" ".*" ".*"
setting permissions for user "biouser" in vhost "bionextgen" ...
Run analysis
With messaging in place, we are ready to run the analysis.
/export/data contains a ready to run example exome analysis, with FASTQ input files in
/export/data/exome_example/fastq and configuration information in
/export/data/exome_example/config. Start the fully automated pipeline with a single command:
$ cd /export/data/work
$ distributed_nextgen_pipeline.py /export/data/galaxy/post_process.yaml
distributed_nextgen_pipeline.py starts processing servers on each of the cluster nodes, using SGE for scheduling. Then a top level analysis server runs, splitting the FASTQ data across the nodes at each step of the process:
- Alignment with BWA
- Preparation of merged alignment files with Picard
- Recalibration and realignment with GATK
- Variant calling with GATK
- Assessment of predicted variant effects with snpEff
- Preparation of summary PDFs for each sample with read details from FastQC alongside alignment, hybrid selection and variant calling statistics from Picard
Monitor the running process
The example data is from a human chromosome 22 hybrid selection experiment. While running, you can keep track of the progress in several ways. SGEs
qstat command will tell you where the analysis servers are running on the cluster:
$ qstat
ob-ID prior name user state submit/start at queue
1 0.55500 nextgen_an ubuntu r 08/14/2011 18:16:32 email@example.com
2 0.55500 nextgen_an ubuntu r 08/14/2011 18:16:32 email@example.com
3 0.55500 automated_ ubuntu r 08/14/2011 18:16:47 firstname.lastname@example.com
Listing files in the working directory will show our progress:
$ cd /export/data/work
$ ls -lh
drwxr-xr-x 2 ubuntu ubuntu 4.0K 2011-08-13 21:09 alignments
-rw-r--r-- 1 ubuntu ubuntu 2.0K 2011-08-13 21:17 automated_initial_analysis.py.o11
drwxr-xr-x 2 ubuntu ubuntu 33 2011-08-13 20:43 log
-rw-r--r-- 1 ubuntu ubuntu 15K 2011-08-13 21:17 nextgen_analysis_server.py.o10
-rw-r--r-- 1 ubuntu ubuntu 15K 2011-08-13 21:17 nextgen_analysis_server.py.o9
drwxr-xr-x 8 ubuntu ubuntu 102 2011-08-13 21:06 tmp
The files that end with
.o* are log files from each of the analysis servers and provide detailed information about the current state of processing at each server:
$ less nextgen_analysis_server.py.o10
INFO: nextgen_pipeline: Processing sample: Test replicate 2; lane
8; reference genome hg19; researcher ; analysis method SNP calling
INFO: nextgen_pipeline: Aligning lane 8_100326_FC6107FAAXX with bwa aligner
INFO: nextgen_pipeline: Combining and preparing wig file [u'', u'Test replicate 2']
INFO: nextgen_pipeline: Recalibrating [u'', u'Test replicate 2'] with GATK
Retrieve results
The processing pipeline results in numerous intermediate files. These take up a lot of disk space and are not necessary after processing is finished. The final step in the process is to extract the useful files for visualization and further analysis:
$ upload_to_galaxy.py /export/data/galaxy/post_process.yaml
For each sample, this script copies:
- A BAM file with aligned sequeneces and original FASTQ data
- A realigned and recalibrated BAM file, ready for variant calling
- Variant calls in VCF format.
- A tab delimited file of predicted variant effects.
- A PDF summary file containing alignment, variant calling and hybrid selection statistics.
into an output directory for the flowcell:
$ ls -lh /export/data/galaxy/storage/100326_FC6107FAAXX/7
-rw-r--r-- 1 ubuntu ubuntu 38M 2011-08-19 20:50 7_100326_FC6107FAAXX.bam
-rw-r--r-- 1 ubuntu ubuntu 22M 2011-08-19 20:50 7_100326_FC6107FAAXX-coverage.bigwig
-rw-r--r-- 1 ubuntu ubuntu 72M 2011-08-19 20:51 7_100326_FC6107FAAXX-gatkrecal.bam
-rw-r--r-- 1 ubuntu ubuntu 109K 2011-08-19 20:51 7_100326_FC6107FAAXX-snp-effects.tsv
-rw-r--r-- 1 ubuntu ubuntu 827K 2011-08-19 20:51 7_100326_FC6107FAAXX-snp-filter.vcf
-rw-r--r-- 1 ubuntu ubuntu 1.6M 2011-08-19 20:50 7_100326_FC6107FAAXX-summary.pd
As suggested by the name, the script can also integrate the data into a Galaxy instance if desired. This allows biologists to perform further data analysis, including visual inspection of the alignments in the UCSC browser.
Learn more
All components of the pipeline are open source and part of community projects. CloudMan, CloudBioLinux and the pipeline are customized through YAML configuration files. Combined with the CloudMan managed SGE cluster, the pipeline can be applied in parallel to any number of samples.
The overall goal is to share the automated infrastructure work that moves samples from sequencing to being ready for analysis. This allows biologists more rapid access to the processed data, focusing attention on the real work: answering scientific questions.
If you'd like to hear more about CloudBioLinux, CloudMan and the exome sequencing pipeline, I'll be discussing it at the AWS Genomics Event in Seattle on September 22nd.
Summarizing next-gen sequencing variation statistics with Hadoop using Cascalog
Improvements in next-generation sequencing technology are leading to ever increasing amounts of sequencing data. With this additional throughput comes the demand for algorithms and approaches that can easily scale. Hadoop offers an open source framework for batch processing large files. This post describes using Cascalog, a Hadoop query language written in Clojure, to investigate quality statistics for variant calling in deeply sequenced regions.
Biological question
The goal is to improve a variation calling algorithm for next-generation sequencing data. We have a densely sequenced region, where each position has thousands of potential base calls. Each position may be a single base, or a mix of of majority and minority variants. We are filtering variants on 3 metrics of quality:
- Quality score — The sequencing technology's assessment of the correctness of a base.
- K-mer score — An estimate of the uniqueness of the region surrounding the base call position, built using khmer. Unique regions are more likely to be sequencing artifacts, while common regions are more likely to be real.
- Mapping score — The aligner's estimate of the reliability of the read alignment.
Each read and position is in a tab delimited file that looks like:
The training data has a set of known variable positions, and details about how the current variant calling algorithm did at each position:
951 T false_positive 0.7
953 A true_positive 4.0
We wanted to generate summary statistics at each position of interest, and look for additional criteria that could be built into the calling algorithm.
Writing cascalog queries
Cascalog is based on the Datalog rule language, a subset of Prolog. You describe the rules of a system and the query optimizer figures out how best to satisfy them; it requires a change of mindset from the more standard approach that you need to write detailed instructions about what to do.
Cascalog provides a high level of abstraction over Hadoop and Map-Reduce, so you focus entirely on writing the query. This post from Antonio Piccolboni compares several Hadoop languages; the post provides a nice side-by-side example of the brevity you can achieve with Cascalog.
The main query defines the outputs, retrieves input data from the
snpdata and location
target files described above, provides a count of reads at each position and base of interest, then averages the kmer, quality and mapping score metrics described earlier:
(defn calc-snpdata-stats [snpdata targets] (??<- [?chr ?pos ?base ?count ?avg-score ?avg-kmer-pct ?avg-qual ?avg-map ?type] (snpdata ?chr ?pos ?base ?qual ?kmer-pct ?map-score) (targets ?chr ?pos ?base ?type) (ops/count ?count) (ops/avg ?kmer-pct :> ?avg-kmer-pct) (ops/avg ?qual :> ?avg-qual) (ops/avg ?map-score :> ?avg-map) (combine-score ?kmer-pct ?qual ?map-score :> ?score) (ops/avg ?score :> ?avg-score)))
A big advantage of Cascalog is that it is just Clojure, so you can write custom queries in a full-featured language. The last two lines of the query define a custom score and its average at a position. The custom score is a linear combination of the min-max normalized scores:
(defn min-max-norm [score minv maxv] (let [trunc-score-max (if (< score maxv) score maxv) trunc-score (if (> trunc-score-max minv) trunc-score-max minv)] (/ (- trunc-score minv) (- maxv minv)))) (defmapop combine-score [kmer-pct qual map-score] (+ (min-max-norm kmer-pct 1e-5 0.10) (min-max-norm qual 4.0 35.0) (min-max-norm map-score 0.0 250.0)))
The final part of the code involves parsing the files and producing the
snpdata and
targets inputs to the query. That code splits each line in the input file and assigns the parts to the variables of interest:
(defmapop parse-snpdata-line [line] (let [[space pos base qual kmer-pct map-score] (split line #"\t")] [space (Integer/parseInt pos) base (Integer/parseInt qual) (Float/parseFloat kmer-pct) (Integer/parseInt map-score)])) (defn snpdata-from-hfs [dir] (let 1 (<- [?chr ?pos ?base ?qual ?kmer-pct ?map-score] (source ?line) (parse-snpdata-line ?line :> ?chr ?pos ?base ?qual ?kmer-pct ?map-score))))
Running on Hadoop
The full project is available on GitHub. To run on a configured Hadoop system, you build the code, copy your input files to HDFS, then run:
% lein deps
% lein uberjar
% hadoop fs -mkdir /tmp/snp-assess/data
% hadoop fs -mkdir /tmp/snp-assess/positions
% hadoop fs -put your_variation_data.tsv /tmp/snp-assess/data
% hadoop fs -put positions_of_interest.tsv /tmp/snp-assess/positions
% hadoop jar snp-assess-0.0.1-SNAPSHOT-standalone.jar
snp_assess.core /tmp/snp-assess/data /tmp/snp-assess/positions
The same code can also run locally without Hadoop. This is extremely useful for testing and development, or for smaller datasets that do not require the distributed power of Hadoop:
% lein deps
% lein run :snp-data /directory/of/varation/data /directory/of/positions
Both approaches generate tabular output with our positions, counts, scores and average metrics:
| 951 | T | 3 | 0.9 | 2.0e-04 | 24.7 | 55.7 | false_positive |
| 953 | A | 10 | 1.5 | 1.6e-02 | 23.1 | 175.5 | true_positive |
Overview and additional projects
Cascalog provided an easy to use abstraction on top of Hadoop, which enabled exploration of densely mapped next-generation sequencing reads for variant detection. The code is free of scaling specific details, and instead focuses purely on the data of interest.
Another example of Cascalog in a biological setting is the answer I wrote to Pierre's question on BioStar, dealing with overlapping genomic segments within Hadoop. The code is available from GitHub as an additional starting point for getting oriented with Hadoop and Cascalog.
Bioinformatics jobs at Harvard School of Public Health
I've recently moved positions to the bioinformatics core at Harvard School of Public Health. It's a great place to do science, with plenty of researchers doing interesting work and actively looking for bioinformatics collaborators. The team, working alongside members of the Hide Lab, is passionate about open source work. Both qualities made it a great fit for my interests and experience.
My new group is currently hiring bioinformatics researchers. The work involves interacting collaboratively with a research group to understand their biological problem, creatively attacking the mountains of data underlying the research question, and presenting the results back in an intuitive fashion. On the programming side, it's an opportunity to combine existing published toolkits with your own custom algorithms and approaches. On the biology side, you should be passionate and interested in thinking of novel ways to advance our understanding of the problems. Practically, all of this work will involve a wide range of technologies and approaches; I expect plenty of next-generation sequencing data and lots of learning about the best ways to scale analyses.
Our other goal is to build re-usable tools for the larger research community. We work extensively with analysis frameworks like Galaxy and open standards like ISA-Tab. We hope to extract the common parts from disparate experiments to build abstractions that help get new analyses done quicker. Tool building also involves automating and deploying analysis pipelines in a way that allows biologists to run them directly. By democratizing analyses and presenting results to researchers at a high level they can directly interact with, science is accelerated and the world becomes an awesomer place.
So if you enjoy the work I write about here, and have always secretly wanted to sit in an office right next to me, now is your big chance (no stalkers, please). If this sounds of interest, please get in touch and I'd be happy to pass along more details.
Next generation sequencing information management and analysis system for Galaxy
Next generation sequencing technologies like Illumina, SOLiD and 454 have provided core facilities with the ability to produce large amounts of sequence data. Along with this increased output comes the challenge of managing requests and samples, tracking sequencing runs, and automating downstream analyses.
Our group at Massachusetts General Hospital approached these challenges by developing a sample submission and tracking interface on top of the web-based Galaxy data integration platform. It provides a front end for biologists to enter their sample details and monitor the status of a project. For lab technicians doing the sample preparation and sequencing work, the system tracks sample states via a set of progressive queues providing data entry points at each step of the process. On the back end, an automated analysis pipeline processes data as it arrives off the sequencer, uploading the results back into Galaxy.
This post will show videos of the interface in action, describe installation and extension of the system, and detail the implementation architecture.
Front-end usage
Researcher sample entry
Biologists use a local Galaxy server as an entry point to submit samples for sequencing. This provides a familiar interface and central location for both entering sample information and retrieving and analyzing the sequencing data.
Practically, a user begins by browsing to the sample submission page. There they are presented with a wizard interface which guides them through entry of sample details. Multiplexed samples are supported through a drag and drop interface.
When all samples are entered, the user submits them as a sequencing project. This includes billing information and a project name to facilitate communication between the researcher and the core group about submissions. Users are able to view their submissions grouped as projects and track the state of constructs. Since we support a number of services in addition to sequencing — like library construction, quantitation and validation — this is a valuable way for users to track and organize their requests.
Sequencing tracking and management
Administrators and sequencing technicians have access to additional functionality to help manage the internal sample preparation and sequencing workflow. The main sample tracking interface centers around a set of queues; each queue represents a state that a sample can be in. Samples move through the queues as they are processed, with additional information being added to the sample at each step. For instance, a sample in the 'Pre-sequencing quantitation' queue moves to the 'Sequencing' queue once it has been fully quantitated, with that quantitation information entered by the sequencing technician during the transition.
Assigning samples to flow cells occurs using a drag and drop jQueryUI interface. The design is flexible to allow for placing samples across multiple lanes or multiplexing multiple barcoded samples into a single lane.
Viewing sequencing results
Running a sequencing machine requires careful monitoring of results and our interface provides several ways to view this data. Raw cluster and read counts are linked to a list of runs. For higher level analyses, interactive plots are available for viewing reads over time and pass rates compared to read density. These allow adjustment of experimental procedures to maximize useful reads based on current machine chemistry.
Analysis pipeline
Utilizing a front end that organizes requests allows sequencing results to be processed through a fully automated analysis pipeline on the back end. The pipeline detects runs coming off of a sequencer, transfers files to storage and analysis machines and manages a number of processing steps:
- Alignment with bowtie or bwa.
- Generation of alignment and read statistics with Picard, the fastx toolkit and SolexaQA.
- Preparation of a summary PDF with detailed statistics about the run and alignment.
In addition to the default analysis, a full SNP calling pipeline is included with:
- GATK recalibration and realignment
- SNP identification with GATK's unified genotyper
- Variant effect prediction with snpEff.
Fastq reads, alignments files, summary PDFs and other associated files are uploaded back into into Galaxy Data Libraries organized by sample names. Users can download results for offline work, or import them directly into their Galaxy history for further analysis or display.
Installing and extending
The code base is maintained as a Bitbucket repository that tracks the main galaxy-central distribution. It is updated from the main site regularly to maintain compatibility, with the future goal of integrating a generalized version into the main source tree. Detailed installation instructions are available for setting up the front-end client.
The analysis pipeline is written in Python and drives a number of open source programs; it is available as a GitHub repository with documentation and installation instructions.
We are using the current system in production and continue to develop and add features based on user feedback. We would like to generalize this for other research cores with additional instruments and services, and would be happy to hear from developers working on this type of system for their facilities.
Implementation details
This work would not have been possible without the great open source toolkits and frameworks that it builds on. Galaxy provides not only an analysis framework, but also a ready to use database structure for managing samples and requests. The front end builds off existing Galaxy sample tracking work, and requires only two new database storage tables.
The main change from the existing sample tracking framework is a generalization of the sample and request relationships. Requests can both contain samples, and be a part of samples so that a sequenced sample is organized as:
By reusing and expanding the great work of the Galaxy team, we hope to eventually integrate useful parts of this work into the Galaxy codebase.
CloudBioLinux: progress on bioinformatics cloud images and data
My last post introduced a framework for building bioinformatics cloud images, which makes it easy to do biological computing work using Amazon EC2 and other on-demand computing providers. Since that initial announcement we've had amazing interest from the community and made great progress with:
- A permanent web site at cloudbiolinux.org
- Additional software and genomic data
- New user documentation
- A community coding session: Codefest 2010
New software and data
The most exciting changes have been the rapid expansion of installed software and libraries. The goal is to provide an image that experienced developers will find as useful as their custom configured servers. A great group of contributors have put together a large set of programs and libraries; the configuration files have all the details on installed programs as well as libraries for Python, Perl, Ruby, and R. Another addition is support for non-packaged programs which provides software not yet neatly wrapped in a package manger or library-specific install system: next-gen software packages like Picard, GATK and Bowtie are installed through custom scripts.
To improve accessibility for developers who prefer a desktop experience, a FreeNX server was integrated with the provided images. Tim Booth from the NEBC Bio-Linux team headed up the integration of FreeNX, and the user experience looks very similar to a locally installed Bio-Linux desktop.
In addition to the software image, a publicly available data volume is now available that contains:
- Genome sequences pre-indexed for search with next-gen aligners like Bowtie, Novoalign, and BWA.
- LiftOver files for mapping between sequence coordinates.
- UniRef protein databases, indexed for searching with BLAST+.
Coupled with the software images, this volume makes it easy to do next-gen analyses. Start up an Amazon AMI, attach the genome data volume, transfer your fastq file to the instance, and kick off the analysis. The overhead of software installation and genome indexing is completely removed. Thanks to the work of Enis Afgan and James Taylor of Galaxy, the data volume plugs directly into Galaxy's ready to use cloud image. Coupling the data and software with Galaxy provides a familiar web interface for running tools and developing biological workflows.
The data volume preparation is fully automated via a fabric install script, similar to the software install script. Additional data sources are easily integrated, and we hope to expand the available datasets based on feedback from the community.
Documentation and presentations
The software and data volumes are only as good as the documentation which helps people use them:
- Bela Tiwari of the NEBC Bio-Linux team has written an excellent introduction to Amazon EC2 and CloudBioLinux. This breaks down the process of signing up for an account, creating a software image, associating data volumes and setting up a graphical server. It's a great place to get started with CloudBioLinux.
- Ntino Krampis, from the JCVI Cloud Bio-Linux project, gave a presentation on CloudBioLinux explaining the motivation behind the project and providing usage examples.
- My presentation on the open source community behind CloudBioLinux from Amazon's Genomic Data workshop. This details the project goals and automated code organization.
Community: Codefest 2010
The CloudBioLinux community had a chance to work together for two days in July at Codefest 2010. In conjunction with the Bioinformatics Open Source Conference (BOSC) in Boston, this was a free to attend coding session hosted at Harvard School of Public Health and Massachusetts General Hospital. Over 30 developers donated two days of their time to working on CloudBioLinux and other bioinformatics open source projects.
Many of the advances in CloudBioLinux detailed above were made possible through this session: the FreeNX graphical client integration, documentation, Galaxy interoperability, and many library and data improvements were started during the two days of coding and discussions. Additionally, the relationships developed are the foundation for better communication amongst open source projects, which is something we need to be continually striving for in the scientific computing world.
It was amazing and inspiring to get such positive feedback from so many members of the bioinformatics community. We're planning another session next year in Vienna, again just before BOSC and ISMB 2011; and again, everyone is welcome.
Go to the CloudBioLinux website for the latest publicly available images and data volumes, which are ready to use on Amazon EC2. With Amazon's new micro-images you can start analyzing data for only a few cents an hour. It's an easy way to explore if cloud resources will help with computational demands in your work. We're very interested in feedback and happy to have other developers helping out; please get in touch on the CloudBioLinux mailing list.
Usage plans for Amazon Web Services research grant
Amazon Web Services provide an excellent distributed computing infrastructure through their Elastic Compute Cloud (EC2), Elastic Block Storage (EBS) and associated resources. Essentially, they make available on demand compute power and storage at prices that scale with usage. In the past I've written about using EC2 for parallel parsing of large files. Generally, I am a big proponent of distributed computing as a solution to dealing with problems ranging from job scaling to improving code availability.
One of the challenges in advocating for using EC2 at my day to day work is the presence of existing computing resources. We have servers and clusters, but how will we scale for future work? Thankfully, we are able to assess the utility of Amazon services for future scaling through their education and research grants. Our group applied and was accepted for a research grant which we plan to use to develop and distribute next generation sequencing analyses both within our group at Mass General Hospital and in the larger community.
Amazon Machine Images (AMIs) provide an opportunity for the open source bioinformatics community to increase code availability. AMIs are essentially pre-built operating systems with installed programs. By creating AMIs and making them available, a programmer can make their code readily accessible to users and avoid any of the intricacies of installation and configuration. Add this to available data in the form of public data sets and you have a ready to go analysis platform with very little overhead. There is already a large set of available AMIs from which to build.
This idea and our thoughts on moving portions of our next generation sequencing analysis to EC2 are fleshed out further in our research grant application, portions of which are included below. We'd love to collaborate with others moving their bioinformatics work to Amazon resources.
Research Background
One broad area of rapid growth in biology research is deep sequencing (or short read) technology. A single lab investigator can produce hundreds of millions of DNA sequences, equivalent in scale to the entire human genome, in a period of days. This DNA sequencing technology is widely available through both on-site facilities as well as through commercial services. Creating scalable analysis methods is a high priority for the entire bioinformatics community; see http://selab.janelia.org/people/eddys/blog/?p=123 for a presentation nicely summarizing the issues. We propose to address the computational bottlenecks resulting from this huge data volume using distributed AWS resources.
An additional aim of our work is to provide tools to biologists looking to solve their data analysis challenges. When the computational portion of a project becomes a time limiting step, we can often speed up the cycling between experiment and analysis by providing researchers with ready to run scripts or web interfaces. However, this is complicated by high usage on shared computational resources and heterogeneous platforms requiring time consuming configuration. Both problems could be ameliorated by scalable EC2 instances with custom configured machine images.
The goals of this grant application are to develop our analysis platform on Amazon's compute cloud and assess transfer, storage and utilization costs. We currently have internal computational resources ranging from high performance clusters to large memory machines. We believe Amazon's compute cloud to be an ideal solution as our analysis needs outgrow our current hardware.
Benefits to Amazon and the community
Developing software on AWS architecture presents a move towards a standard platform for bioinformatics research. Our group is invested in the open source community and shares both code and analysis tools. One common hindrance to sharing is the heterogeneity of platforms; code is developed on a local cluster and not readily generalizable, hence it is not shared.
By building public machine images along with reusable source code, a diverse variety of users can readily use our code and tools. As short read sequencing continues to increase in utility and popularity, a practical ready-to-go platform for analyses will encourage many users to adopt parallelization on cloud resources as a research approach. We have begun initial work with this paradigm by developing parsers for large annotation files using MapReduce on EC2.
Having the ability to utilize AWS with your support will help us further develop and disseminate analysis templates for the larger biology community, enabling science both at MGH and elsewhere.
Trimming adaptors from short read sequences
One common post processing step for next generation, or short read, sequencing is to remove adaptor sequences. Sample preparation for sequencing often involves selection of a particular fraction of interest like expressed genes, microRNAs, or copy number variant regions. Selecting, amplifying, and sequencing these regions can result in read sequences containing a common tag that needs to be identified and removed before continuing with other analysis steps like alignment to the genome. This is often the case when the reads of interest are small like in short RNAs or small sequence tags.
We start with a short known sequence (the adaptor) that will be present in our sequences of interest. For each read, we want to determine the position of this adaptor and remove it. Additionally, reads may contain the adaptor but differ by one or more bases; these differences are likely due to sequencing errors. So our solution needs to allow some fuzzy matching of the adaptor to the read sequences.
My first attempt involved using fuzzy string matching. In Python, several libraries exist to tackle this problem. The normal use case is in identifying misspelled words in text. For instance, you can calculate Levenshtein (edit) distance or use difflib in the standard library; Stack Overflow has a nice discussion of the different modules. Ultimately this approach proved to be a dead end; the properties that make English words similar do not overlap well with those differentiate DNA sequences.
That realization led me to tackle the problem by computing local alignments of the adaptor to each sequence. This effectively captures the biological intuition you need to trim a sequence with less than a specified number of errors. The downside to this rigorous approach is that it will be slower than purely string based methods.
The Biopython library contains a Python local alignment function suitable for quick alignment of short regions. We use this to align an adaptor region to a sequence and calculate the number of differences in the aligned region. To handle a common case where we can find the exact adaptor sequence, we first do an string match. This avoids the alignment overhead in many cases:
from Bio import pairwise2 def trim_adaptor(seq, adaptor, num_errors, right_side=True): gap_char = '-' exact_pos = str(seq).find(adaptor) if exact_pos >= 0: seq_region = str(seq[exact_pos:exact_pos+len(adaptor)]) adapt_region = adaptor else: seq_a, adaptor_a, score, start, end = pairwise2.align.localms(str(seq), str(adaptor), 5.0, -4.0, -9.0, -0.5, one_alignment_only=True, gap_char=gap_char)[0] adapt_region = adaptor_a[start:end] seq_region = seq_a[start:end] matches = sum((1 if s == adapt_region[i] else 0) for i, s in enumerate(seq_region)) # too many errors -- no trimming if (len(adaptor) - matches) > num_errors: return seq # remove the adaptor sequence and return the result else: return _remove_adaptor(seq, seq_region.replace(gap_char, ""), right_side)
If the alignment contains fewer than the specified number of errors we've found an adaptor and remove it, returning the trimmed sequence. The attribute
right_side allows us to specify whether the trimming should be done on the right (3′ end) or the left (5′ end):
def _remove_adaptor(seq, region, right_side=True): """Remove an adaptor region and all sequence to the right or left. """ if right_side: try: pos = seq.find(region) # handle Biopython SeqRecords except AttributeError: pos = seq.seq.find(region) return seq[:pos] else: try: pos = seq.rfind(region) # handle Biopython SeqRecords except AttributeError: pos = seq.seq.rfind(region) return seq[pos+len(region):]
Here is an example script using this function along with Biopython to trim reads from a FASTQ format file.Each read is analyzed to determine if it contains the adaptor, with up to 2 errors. If the adaptor is identified and trimmed, the final useful read is written to a FASTA format output file.
from __future__ import with_statement import sys import os from Bio import SeqIO from adaptor_trim import trim_adaptor def main(in_file): adaptor_seq = "TCGTATGCCGTCTTCTGC" num_errors = 2 base, ext = os.path.splitext(in_file) out_file = "%s-trimmed.fa" % (base) with open(in_file) as in_handle: with open(out_file, "w") as out_handle: for rec in SeqIO.parse(in_handle, "fastq"): trim = trim_adaptor(rec.seq, adaptor_seq, num_errors) if len(trim) > 0 and len(trim) < len(rec): rec.letter_annotations = {} rec.seq = trim SeqIO.write([rec], out_handle, "fasta")
You can get the full trimming code, along with tests, from GitHub. The script takes about a half hour to process 15 million 36bp reads. Other current trimming approaches are often integrated into the aligners themselves: for instance, NovoAlign appears to have similar alignment based trimming capabilities.
Finding and displaying short reads clustered in the genome
Short read, or next-generation, sequencing is becoming increasingly prevalent in the day-to-day lives of bioinformatics researchers. Programs such as Bowtie, CloudBurst, Maq, and Arachne are available to align short reads to a reference genome. Once you have these alignments, the data analysis work begins. During a recent project, I was interested in looking at overlapping read groups across a genome; this post describes a method for collecting and visualizing those groups.
Starting with a file from an alignment program, the first step is to write a Python generator that returns information about the alignments. Since there are many different aligners, below is some Python pseudocode which describes the function:
def parse_alignment(align_file, errors_allowed=0): with open(align_file) as align_handle: for (read_id, match_id, match_start, match_end, errors) in \ your_parser.read_iterator(align_handle): if (errors <= errors_allowed): yield read_id, match_id, match_start, match_end
It parses a line, checks if it has an acceptable number of alignment errors, and yields a unique id for the read, an id for the match like a chromosome or contig name, and the start and end coordinates of the match. We can use this generator to build our representation of overlapping reads with the help of the excellent bx-python library. bx-python contains a Python and C implementation of clustered interval trees. The function below uses the interface to generate
ClusterTree objects for each chromosome/contig in your alignment:
import collections from bx.intervals.cluster import ClusterTree def build_cluster_trees(align_generator, cluster_distance): # arguments to ClusterTree are: # - Distance in basepairs for two reads to be in the same cluster; # for instance 20 would group all reads with 20bp of each other # - Number of reads necessary for a group to be considered a cluster; # 2 returns all groups with 2 or more overlapping reads cluster_trees = collections.defaultdict(lambda: ClusterTree(cluster_distance, 2)) for read_id, match_id, start, end in align_generator: cluster_trees[match_id].insert(start, end, read_id) return dict(cluster_trees)
The generated trees will readily provide a list of start and end coordinates for a clustered region, along with all of the reads that map to that region:
for chrom, cluster_tree in cluster_trees.items(): for start, end, read_ids in cluster_tree.getregions(): # do something with your read cluster
My interest was in visualizing the reads in these clusters along with the frequency of each read. To do so, I generated two python dictionaries which map the
read_id identifiers, used in the grouping, to the chromosome location of the read (
location_db) and the number of times a read was found in the raw short read results (
freq_db). Practically, these are implemented as BerkeleyDB key value stores, to avoid having to keep all of the read information in memory.
With this information, we can use matplotlib to visualize the reads. Frequencies are represented using a color map. Each read in a group is plotted using horizontal bar graphs:
import pylab def plot_reads(cluster_name, read_ids, location_db, freq_db): read_info = [] for read_id in read_ids: start, end = location_db[read_id] freq = freq_db[read_id] read_info.append((start, end, freq)) read_info.sort(reverse=True) min_freq = min(l[2] for l in read_info) max_freq = max(l[2] for l in read_info) freq_cmap = pylab.get_cmap('Blues') pylab.clf() for rindex, (start, end, freq) in enumerate(read_info): freq_percent = float(freq - min_freq) / float(max_freq - min_freq) color = freq_cmap(freq_percent) if freq in [min_freq, max_freq]: label = "Frequency: %s" % (freq) else: label = None if label and label not in labels_done: labels_done.append(label) else: label = None pylab.barh(rindex, end - start, left=start, height=0.8, color=color, label=label) pylab.title(cluster_name) pylab.xlabel("Coordinates") pylab.ylabel("Reads") pylab.legend(loc='upper right') pylab.yticks(()) out_file = "%s.png" % (cluster_name) pylab.savefig(out_file)
The resulting graph for an example region provides a quick visual summary of read coverage, overlap, and reliability:


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Auto News
AutoGuide News Blog
The AutoGuide News Blog is your source for breaking stories from the auto industry. Delivering news immediately, the AutoGuide Blog is constantly updated with the latest information, photos and video from manufacturers, auto shows, the aftermarket and professional racing.
It's obvious that the Volkswagen XL1 isn't made for the general automotive consumer, but with a price tag of $146,600, is it made for anyone at all?
Top 10 Cars of the 2013 Geneva Motor Show
It's business as usual to have supercars in spades at the 2013 Geneva Motor Show. Exactly how awesome was it? Here's a hint, the 1,030 hp carbon bodied Koenigsegg Hundra didn't even make the list. We're just using it to fill space.
It's not an official Alfa Romeo, but maybe it should be. Called the Gloria Concept it made its debut at the 2013 Geneva Motor Show this week thanks to the work of some inspired students.
Pagani doesn't have a new model or a new engine on display at this year's Geneva Motor Show. Rather, the Italian supercar maker used the opportunity to showcase a new Sonus Faber sound system for the exotic (and hard to pronounce) Huayra.
You didn't think we would head to Europe for the 2013 Geneva Motor Show and not snap photos of all the race cars, did you?
It's hard to imagine a rugged version of an Italian bull, but the ItalDesign Giugiaro Parcour means you don't have to. Built in celebration of ItalDesign Giugiaro's 45 years in business and unveiled this week at the Geneva Motor Show, the design house decided to make something that didn't exist until now.
That Porsche Cayman is going to be nervously checking its rear-view mirror later this year, with the lightweight Alfa Romeo 4C finally arriving. Continue Reading…
The C-segment crossover market is a hot place for consumers, and Hyundai is right at the forefront with this, the new Hyundai Tucson.
Subaru is exploring every avenue for fuel efficiency with its new diesel-hybrid Viziv concept.
Could this be Volkswagen's Prius? A fuel efficient halo car, the XL1 is expected to deliver a whopping 261-mpg thanks to its tiny engine, low weight and slip-stream design. Continue Reading…
With a bolder look, it's easy to see that this is no ordinary GranTurismo. Focused for the track, Maserati unveiled its new MC Stradale at the Geneva Motor Show this week, looking like a UFC fighter anxious for its title-card bout. Continue Reading…
Let's face it: Bugatti needs no introduction, especially not at the 2013 Geneva Motor Show. No grand debuts, no jaw-dropping unveilings; the French automaker simply brought out three Veyrons of all shades to show off to the world.
The 2013 Geneva Motor Show was full of halo cars for several brands, as even Ferrari and McLaren showed off their most powerful vehicles yet. Yet another iconic brand debuting the most potent car it's ever made was none other than Rolls-Royce.
If there was ever a time where less is more, and simpler is better, this would be it. One of the most beautiful cars to ever roll off a factory assembly line has received the Mansory treatment, and what it does to your eyes might constitute a crime.
Making its world premiere earlier this year at the North American International Auto Show in Detroit, Infiniti brought its new Q50 luxury sports sedan to the Geneva Motor Show this week sporting an all-new diesel engine.
Kia showcased a possible rival for the Nissan Juke compact-crossover this week at the Geneva Motor Show, features funky styling, a turbocharged engine and one extra trick up it's sleeve: a hybrid powertrain. Continue Reading…
Touring Superleggera makes some very custom vehicles, and this model, the Disco Volante, apparently takes 4,000 man hours to complete. That's a long time to make what essentially looks like a Cayman on top of an Alfa Romeo 8C.
Diesel. Hot-hatch. Not much more needs to be said about the upcoming Volkswagen GTD, which will eventually make its way to American shores, giving enthusiasts a crazy amount of torque to go with practical hatchback dimensions.
When we first previewed the Rinspeed microMAX in December, we called it "your living room on wheels," and it turns out, it's that much and more.
Overshadowed by the new Golf's debut at the 2013 Geneva Motor Show, Volkswagen treated its Polo R WRC street car like yesterday's news, tucking and hiding it from plain sight in its massive booth.
An open-top FR-S is finally here, although right now, the Toyota FT-86 Open is still just a concept.
What happens when you take the Aston Martin Rapide and transform its body so it's even longer? You essentially get an Aston Martin Rapide wagon, or as Bertone likes to call it, the Jet 2+2.
BMW has revealed its new 3-Series Gran Turismo at the 2013 Geneva Motor Show this week and despite rumors that it wouldn't make it to our shores, it has now been confirmed for North America.
It's been a decade since the Enzo launched and changed the automotive landscape, now Ferrari is hoping for a repeat with a supercar it calls LaFerrari.

subset: commoncrawl


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Drug charge father escapes death penalty (From The Argus)
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Brighton man Julian Ponder escapes death penalty for drug charge
3:10pm Wednesday 9th January 2013 in News By Emily Walker, Chief reporter
A father is no longer facing the firing squad after a charge of selling drugs against him was dropped.
Brighton father Julian Ponder had been accused in Indonesia of possessing drugs and selling drugs – an offence punishable by the death penalty in the country.
But yesterday (January 8) prosecutors in Bali dropped the more serious charge.
Prosecutors asked for him to instead be sentenced to seven years in prison for possession of drugs and for him to be fined $100,000 (£62,000).
Ponder and his partner Rachel Dougall – who have a six-year-old daughter together and a flat in Eastern Terrace, Kemp Town, Brighton – were accused of being part of "the British gang of four" accused of trying to smuggle £1.6million of cocaine into the country from Thailand last May.
Dougall, 39, was jailed for a year last month after being convicted of failing to report the bringing of drugs into Indonesia.
She was told by Denpasar District Court that she could be on her way back to the UK by April, to be reunited with her daughter, because she has already been in jail for eight months awaiting trial.
Relieved Grandmother Lindsay Sandiford, 56, is accused of trafficking £1.6million worth of cocaine onto Bali.
Prosecutors last month asked for her to be imprisoned for 15 years rather than face execution following her co-operation in the case.
Sandiford was paraded in front of the press after 10.6lb (4.8kg) of cocaine was supposedly found in the lining of her suitcase at Bali's Denpasar Airport last May.
Dougall's father Barry – who lives in Haywards Heath – told The Argus yesterday that he was relieved his daughter had received a lenient sentence.
He said: "I was expecting it to be a lot worse, so I'm quite relieved.
On trial
"I haven't been able to speak to Rachel yet but I'm hoping she'll be able to get in touch soon."
Following the pair's arrests, Dougall's mother Stella Woodley, 75, of Withdean Rise, Brighton, and brother Asa, 51, said they were "devastated".
Former antiques restorer Ponder and Sandiford's trials are both continuing separately.
A fourth Briton accused of being part of the alleged gang, long term Bali resident Paul Beales, was sentenced to four years in prison last month for possession of a small amount of hashish.
See the latest news headlines from The Argus:
- City's property prices rise by 10% in a year
- Council hailed for project that helps families
- Council eyes Israel boycott
- Anger over disabled bay admin fee
- Neighbour asks council to pull down classroom
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The Cardinal defeat No. 2 Cal 6-4 to capture conference title.
SAN JOSE, CA – The Stanford women's water polo team
defeated second-ranked Cal to win the Mountain Pacific Sports
Federation Conference Championship title. The Cardinal, who
were 0-3 against the Golden Bears this season before Sunday's
game used a four-goal first period to take an early lead and win
the championship game 6-4 for the first time in the history of the
Freshman Julie Gardner and junior Kris Koblik led the Cardinal
attack, scoring two goals each. Sophomore Margie Dingeldein
and junior Anne-Marie Keenan each recorded one goal to give
Stanford a total of six.
Margie Dingeldein, Julie Gardner and freshman Cardinal
goalkeeper Jackie Frank were all named First-Team MPSF
The Cardinal turned in a record of 5-0 in the MPSF Conference.
The squad's overall record improves to 22-4. Stanford will play
one more regular season game against UC Davis on April 14th.
Stanford 6, California 4
1 2 3 4 Total
California 1 1 1 1 4
Stanford 4 1 1 0 6
Scoring Summary
California: Breena Fleener 2, Beth Irwin, Julie Arnold
Stanford: Julie Gardner 2, Kris Koblik 2, Margie Dingeldein,
Anne-Marie Keenan


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State Board Places Two Proposed Policies Out on Comment
Posted: December 16, 2003
CHARLESTON, W.Va. - The West Virginia Board of Education placed two policies, 2520.15, and 2510, out on comment during its December meeting.
Policy 2520.15 would establish a framework for standards related to providing high quality pre-kindergarten programs with the public school system, as well as any program that collaborates with local boards of education to provide WV Pre-K services. The policy will be on comment until Jan. 11, 2004.
The purpose of Policy 2510 is to establish the regulations for all education programs that are designed to improve the quality of teaching and learning in the public schools and ensure that equal educational opportunities exist for all students. This policy will be on comment until Jan. 30, 2004.
To read the proposed policies and submit comments, please visit http://wvde.state.wv.us/policies/.
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text: Excerpted from The Trouble with Tink (Disney Fairies) by Kiki Thorpe; illustrated by RH Disney. Copyright © 2006 by Kiki Thorpe. Excerpted by permission of RH/Disney, a division of Random House LLC. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.


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Wonderful news from the Hillcrest dormitory early this morning, as guard Dan Bohall was arrested for public intoxication. In a dorm he doesn't live in. On the crapper. Yep.
According to police, Daniel Stephen Bohall, 21, a sophomore guard from Sioux City, was found lying in a locked stall with no shirt on at 5:09 a.m. Police said Bohall did not initially wake up when he was moved and when he did wake, he had poor balance, slurred speech and smelled of alcohol. Bohall allegedly admitted to drinking.
Clearly, Bohall was looking for Clint Huntrods' tried and true winning formula of severe intoxication and bodily fluids, but we've got to say that unless some additional evidence comes out that he was at least decorated, Mr. Huntrods remains Captain Awesome.


text:
Claire Danes & Hugh Dancy: 2014 Producers Guild Awards Couple
Stepping out for an evening of excitement, Claire Danes attended the 2014 Producers Guild of America Awards on Sunday (January 19).
The "Homeland" beauty donned a white pencil skirt and silver top with gold heels as she and her hubby Hugh Dancy made their way down the red carpet.
Meanwhile, on Saturday evening (January 18), Miss Danes gushed about her former "My So-Called Life" costar Jared Leto at the SAG Awards.
When asked about the success of the "Dallas Buyers Club" actor, Claire exclaimed to E! News Giuliana Rancic, "It's great, I actually haven't seen him yet, but it will be fun. I'm very proud of him."
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text:
The Unicel C-4637 R is a pool and spa water filter cartridge that is the correct compatible replacement for the following filters:
The Unicel C-4637 Comp. Replacement Pool Filter has an outer diameter of 4 15/16 inches, with a length of 13 5/16 inches. This Unicel pool filter has an open top and bottom with both having diameters of 2 1/8 inches. This Unicel C-4637 filter contains 37 square feet of filter media.


text:
21-05-2010 15:59
I installed the brandnew Maps application version 3.04 on my Nokia E72, but when I start the application, the phone reboots. I installed the update through the built-in Software Updates feature.
Anybody has the same problem ?
Can I start "from scratch" ? I can't remove the Maps application altogether, it seems...
I'm using the last E72 firmware...
21-05-2010 18:43
same problem here, installed the new version through software update on my n97 mini with sw 10.0.020.227.02
when i start the application the first icon-screen appears looking normal, but shortly after that the screen 'fades' and the phone seems to do a soft reset displaying the start-screen with logo for quite a while before the main-screen appears. sometimes the phone 'locks' on the logo-screen and i'll have to remove the battery to get it to start correctly.
any ideas anybody?
21-05-2010 19:52
exactly the same here with E52 latest firmware , after the upgrade to 3.04 maps is resetting everytime u start it !!
21-05-2010 21:26
I'm also facing the same problem on my E72 v031.023. I tried installing by the following ways:
In all the above cases the phone restarts every time I start new Maps v3.04.
Minsartp: For reverting to the previous version go to App. Manager > Installed Apps. Uninstall everything related to Maps e.g. Nokia Maps, Ovi Maps etc. That way it will revert to the original version of Maps that was installed as part of the firmware. That way its working on my phone now.
If anyone has any idea what the problem might be, please do let us know.
22-05-2010 2:08
I have the same problem with the new Maps 3.04 on my N97 (firmware v20.0.019) I downloaded the Maps update using the Software Update application. As per the other posts, the application just about launches but as soon as Maps finishes loading, the phone crashes and usually reboots itself. Once, the phone crashed and powered off.
I've uninstalled 3.04 so my phone has gone back to its default v3.01. How can I get the stable v3.03 back?
22-05-2010 9:24
I have the same problem on my Nokia E72, but if I disable the Wifi/location in GPS options the problem just gone.
PS: Sorry for my english
22-05-2010 10:00
Kudos yuserneim. After I disabled Wi-Fi/Network in GPS > Positioning settings > Positioning methods, starting Maps v3.04 no longer restarts the phone and is now working fine on my E72.
22-05-2010 10:49
the same happened to me. I deleted cities, all the ovi maps stuff; reinstalled etc etc etc.
Indeed the temp fix is to disable WiFi/Network based position methods.
Maybe if you just delete the added ovi maps file: Ovi Maps WiFi/network positioning (3.02(6)) it also 'fixes' this problem.
Something to try next here..
22-05-2010 19:21
disabling the wifi/network positioning setting on my n97 mini made everything start working again!
24-05-2010 15:05
I have the same problem with my N97 (v 21.0.045) and latest Nokia Maps 3.04. Disabling WLAN/Network seems to help but I would like to have a fix for the software.
For those who don't know how to disable that, just go to settings, phone, application settings, navigation, navigation methods and uncheck the Wi-Fi/Network from there.
24-05-2010 16:06
One step ahead ... but we're not there yet .
I tried the suggestions so far: after disabling Wifi/Network as a GPS Positioning method, MAPS just closes after a few seconds. However, it doesn't reboot my phone anymore.
@roelandjanssen: where did you find the ovi maps file: Ovi Maps Wifi/network positioning (3.02(6)) ? Was it installed with Maps version 3.04 ? I did see that 3.03 doesn't include a Wifi/Network positioning method. Apparently, it was introduced in 3.04.
After you have installed Maps 3.04, which applications in your list of "installed applications" (Ctrl. Panel / App. mgr. / Installed Applications) look like Maps-related applications ?
26-05-2010 16:27
Some problem here with a N97 mini (FW: 10.0.020). Tried to delete map data and reinstalled did not solve the issue.
Disabling the "Wi-Fi/Network" positioning methode fixed the restarting problem.
27-05-2010 13:04 - edited 27-05-2010 13:07
Trying to reproduce that problem, without success so far
@OliverLa and Minsartp: Could you please help and answer few questions:
- from which Maps version did you upgrade to Maps 3.04?
- what other applications/widgets you have installed on device - please list all
- what are the positioning settings: Location/Positioning/Positioning server - what is defined?
28-05-2010 9:44 - edited 28-05-2010 10:27
Can all those who are affected by this issue send me a Private Message with the following information:
With this information I can start investigations with R&D.
Thanks for your help and apologies for the problems this has caused.
29-05-2010 6:04
30-05-2010 12:14
Disabling WiFi did not help me, either. I had to remove all related installations, reverting to 3.03. Strange.
01-06-2010 22:56
@cbidlake: I am having the same issues on the N97, and I just sent you a private message with all the information you requested.
14-06-2010 7:44
I have the same problem with my Nokia N97 mini. The problems started when I installed a WLAN-hotspot-software (JoikuSpot Premium Edition 2.40)). Could this software be the reason to the problem?
14-06-2010 15:10
Could be related, who knows? I also have JoikuSpot Premium on my N97. But then again, how many people have the same settings?
14-06-2010 15:30
I also have "JoikuSpot Premium Edition" installed on my E72.


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yes, this setup looks really stupid, but as long as i dont hand hold, it works really, really well.
Again...it always give me great laugh when I see this Kinda likes shooting 1D X with Canon EF 75-300mm f/4-5.6 III
Why do we buy mirrorless again? Better AF speed? Better tracking system?


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718 Balboa Ct(1st fl)100ft. to Ocean Sand; Sept. openings in Mission Beach
- bedrooms 2
- baths 2
- sleeps 6
- pool no
- hot tub no
- pets allowed yes
- type Cottage
- community South Mission Beach
rates (USD)
/ night
$ 1,500 - $ 2,800 / week
$ 2,500 - $ 4,000 / month
vacation rental description
718 Balboa Court, (lower unit), South Mission Beach - 100 feet to the ocean with a large front patio. (may combine with upper unit 720 for large groups)
NOT ON CRAIG'S LIST - Book only thru DAVE CATALINO on this site or SAN DIEGO BEACH RENTALS. There is no air conditioning.
This two bedroom, two bath unit is 1000 sq. ft., two units from the ocean, 1½ blocks from the bay. The French doors off the dining room open onto a large slate tiled patio which has a view of the beach area if you are tall. The patio is furnished with teak table and chairs, umbrella, teak lounge chairs, and grill.
The inside of the unit is decorated with a cottage feel. The master bedroom has a queen bed. The second bedroom has two twin beds. The kitchen has BBQ and basic kitche items for meals. The kitchen, living room and dining room all open onto each other, and to the large front patio.
vacation rental location
property availability
- dates with deals
- unavailable dates
Last modified on 08-18-2014 at 12:50 PM.
email owner view full calendar
David Catalino 619-339-7202
cottage rental amenities
1 Queen Bed
2 Twin Beds
1 Couch (folds out to a queen size bed, for tow adults)
Wireless internet access - 2 TVs but no DVD or VCR
Small garage with parking for one vehicle
No Airconditioning - Open all windows and you'll be cool!
Washer/dryer in the garage.
Beach chairs; boogie boards and sand toys are shared with the upsairs unit.
It is a smoke free unit and patio, if you smoke stay else where.
Maid sevice upon departure is included.
activities and attractions in Mission Beach San Diego Area
Surfing camp, golf, sailing, water skiing, charter boat fishing, jet
skiing, bike riding and whale watching(in season).
Sea World - 5 min. drive;
San Diego Zoo - 10 min;
Leggoland - 30 min;
Midway Aircraft Carrier Museum - 15 Min;
Balboa Park Gas Lamp District, - 15 mins;
LaJolla Playhouse, Birch Aquarium/Scripps Institute of Oceanography,
San Diego Wild Animal Park, Torrey Pines Golf/Reserve, Palomar
Observatory, - 30 to 60 mins.
Disneyland, Knotts Berry Farm, the Queen Mary, Discovery Science
Center are 90 mins. drive. J. Paul Getty Museum, Norton Simon
Museum, Hollywood and the Sunset Strip are within a two to three
hour drive.
vacation rental rates
Sept. to May rates - 3 night min. $200 to $350 per night depending on date.
Summer 2013
June - weekly $1800 to $2400
July - weekly $2400 to $2800
August - weekly $2200 to $2600
(plus tax of 10.5%)
There is also a $300 security depoist required at the time the reservation is made. This deposit will hold your reservation. Payment in full is due 60 days prior to arival.
David Catalino 619-339-7202
accepted payment methods
Prior to paying a reservation deposit, always verify payment instructions by calling the phone number displayed on this listing page. By contacting the owner directly and paying by an approved method - credit card, paypal, check or bank transfer - your reservation payment will be protected up to $1,000.Learn more in our Security Center
Top things to do in Mission Beach
- Beach
- Swim
- Surf
Top things to do on a budget
- Belmont Park
- Roller Coaster
Top things to do with a family
- Sea World
- San Diego Zoo
- Leggo Land
David Catalino , owner


text: Central Catholic Takes Down Altoona 10/19/2013 12:25 AM 10/21/2013 09:59 PM The Mountain Lions get shut out, 38-0. The Mountain Lions get shut out, 38-0. Copyright 2013 Nexstar Broadcasting, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed. Page: [[$index + 1]] highschool sports hsfaltoona sportsbeat


text:
|REV. and MRS. LARRY HOPKINS
LIMA – The Rev. Larry Hopkins and his wife, Sharon, will celebrate their 50th wedding anniversary by renewing their vows at 2 p.m. Aug. 19 at the Forest Park United Methodist Church in Lima. An open house until 4 p.m. will follow.
Larry Hopkins and the former Sharon Rasey exchanged vows Aug. 19, 1962 in the former Evangelical United Brethren Church in Bryan during a ceremony officiated by Dr. John Searle, the Rev. Marion Hanover and the Rev. Harry Troutner.
The couple have three daughters and a son: Cheri (Robert) Estle of Oakwood, Lori (Keith) Belcher of Lima, Wendi (Jon) Phillips of Elyria and Dr. Kevin (Connie) Hopkins of North Ridgeville. There are eight grandchildren.
The Rev. Hopkins is retired from 45 years of pastoral ministry with the United Methodist Church. Sharon is retired after 32 years teaching in the public school system.
To read the rest of this article please subscribe or sign in


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Big Juicy Love Video - All Worked Up on truTV.com on truTV.com Video
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The Full Throttle Saloon security team is on the hunt for a guy groping the bartenders.
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Although he was killed, this video represents a small victory for bulls everywhere.
Bathroom Stall Four-way Video - on truTV.com on truTV.com Video
Two girls, one stall. Oh, and two other guys, too. According to one girl, they're homies - but she's the only one left for security guards after her homies leave her.
Horse Love Hurts on truTV.com Video
You call that mounting a horse? See how it's really done.
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Jay and England investigate a woman selling drugs, and find out it's actually a man selling urine.
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What starts out as a one-sided fight in Russia, ends in a tug-of-war over a johnson.
Holy Matrimony Video - Rehab on truTV.com on truTV.com Video
Witness the celebration of a marriage of convenience.
Employee Runs Fake Pregnancy Scam on truTV.com Video
When times are tough, some people fake a pregnancy and steal from their employer.


text:
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Category Archives: Clay Workshop
Miniature Clay Food – Vanilla Flower and Bean Pods
My newest dollhouse miniature clay food creation is the vanilla flower and bean pods In Singapore, you may be able to find the bean pods in local baking stores. But I've not been to any baking stores for a long … Continue reading
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Clay Class Singapore – My Christmas Treats Miniatures Class
My latest clay class in Singapore featuring christmas treats in 1:12 dollhouse miniature scale is now available! I'm really excited to introduce this latest clay class as I've been working on it for a little while now. Bring on the … Continue reading
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Air Dry Clay Classes Singapore
Ever wondered how realistic looking food miniatures are created using air dry clay? Air dry clay craft classes are available in Singapore now. Specifically targeted for adults, these craft classes focus on using air dry clay to create highly realistic … Continue reading
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text:
With great pleasure, I write to announce two wonderful additions to the Scholars' Lab team! Purdom Lindblad will join us on December 30th as our much-anticipated Head of Scholars' Lab Graduate Programs (including, among other programs, Praxis and the Praxis Network), and Laura Miller will shortly take on a dual position coordinating public programs in…. More.
Welcoming Katina Rogers!
Today, we're very happy to announce that Dr. Katina Rogers is to join the Scholarly Communication Institute and Praxis Program teams at the Scholars' Lab. Katina comes to us from an appointment at the Alfred P. Sloan Foundation, where she has been responsible for a number of operational areas and has contributed to the strategic…. More.
seeking a Senior Research Specialist for SCI
I'm pleased to share a formal job posting for an 18-month research-and-writing position with the Scholarly Communication Institute. The posting is limited to current employees of the Unversity of Virginia for four business days, after which it will open more broadly.* It's a short-term position, but an excellent opportunity to work with SCI leadership and…. More.
Welcoming Eric Johnson!
We're proud to announce that Eric Johnson will join the faculty of UVa Library's Digital Research and Scholarship department this July as Head of Outreach & Consulting. In his new role, Eric will direct day-to-day public services in the Library's vibrant Scholars' Lab and help to coordinate our intellectual programming as well as our transformative…. More.
Welcoming David McClure!
We're delighted to welcome our new Web Applications Specialist, David McClure, to the Scholars' Lab R&D team. David graduated from Yale University with a degree in Humanities in 2009, and has been working as an independent web developer in San Francisco, New York, and Madison, Wisconsin for the last few years. David is the creator…. More.
Welcoming Jeremy Boggs!
We're thrilled to announce that Jeremy Boggs will be joining the Scholars' Lab and the staff of UVa Library's Digital Research & Scholarship department this June, in the role of Humanities Design Architect. Jeremy comes to us from the wonderful Center for History and New Media (CHNM) at George Mason University, where he serves as…. More.
Welcoming Eric Rochester!
The Scholars' Lab is very pleased to welcome Dr. Eric Rochester as the new Senior Developer on our R&D team! Eric is an accomplished computational linguist and digital humanities scholar and developer. His past work includes appointments with the Oxford University Press and Georgia's Linguistic Atlas projects, as well as consultancies and programming positions at…. More.
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- A (Digital) Declaration of Independence
- Learning Ruby: Opening Moves
- Check Out Copyipsum


text: Last offseason, when we picked up Mark Kotsay, I seem to recall that there was an important discourse about the state of the team, the look of our roster, and the face the team puts forth to it' s fans. The roster is ever-changing as players (like Kotsay) come and go, and every player we pick up can play an important role (like Kotsay did). And just like last year, we've picked up several new players this offseason, and I think it's important that we take the time to again ask the question: Do they have hot wives?


text:
Shoreline erosion is an issue of concern on many rivers. How people handle their boats is pivotal to reducing shoreline erosion. Reducing the speed and resulting wake, especially during high waters, is paramount. Modern power boats operate in three different speed zones:
Displacement Speed is the slowest speed at which boat operates with bow down. Very little wake is produced.
Transition Speed occurs when power is applied resulting in a rising bow. The engine is using its maximum amount of fuel as it attempts to get the boat on plane. The largest wake is produced as the boat is plowing through the water.
Planing Speed is when the boat reaches planning speed as the bow drops, fuel consumption reduces, and only a small portion of the hull contacts the water. The wake decreases in size from its maximum elevation during transition speed.
A five inch wake is a good threshold to reduce shoreline erosion while still providing a reasonable speed for boaters. Hydrologists estimate that a wake ten inches high is five times as destructive to the shoreline as a five inch wake. They further estimate that a wake 25 inches in height has a destructive potential 30 times greater than a five inch wake.
Many runabouts and larger fishing craft create a wake approximately 10 inches high when they are on plane. Large displacement hulls, such as cruisers and houseboats, can create a wake of about 25 inches high.
Wake height especially becomes important during periods of high water. Boat operators need to slow down and make sure their wakes are five inches or less. Remember, as a boat operator you are legally responsible for your wake under both federal and state law.
For additional information visit the Department of Natural Resources.
Shoreline areas provide a unique ecological zone that is required for certain plant and animal species. Destroying this to replace it with a lawn and or other structures reduces the health of the water as well as its surrounding habitat. Eutrophication, the process where lakes change because of an overabundant supply of nutrients, can occur. Changing the diversity of the shorelines such as reducing buffer zones increases the amount of phosphorus, nitrogen, and other materials entering the lake. These nutrients cause the rapid growth of aquatic weeds and algae. This growth leads to the buildup of muck on the bottom leading to a transferring of fish populations from sport fish such as bass and walleye to rough fish like carp. This process can be slowed or even reversed by proper use of management practices.
Outdoor recreation is dependant on a healthy and attractive environment. Recreation pursuits should allow people to enjoy the outdoors without damaging the environment. The following are basic principles that property owners can observe to help improve shoreline quality as well as water quality.
Buffer Zones
Areas adjacent to bodies of water should be left in its natural condition. Buffer or filter strips provides many unique benefits to the aquatic ecosystem. They include:
Buffer zones should be as wide as possible. For agricultural lands, a minimum width of 50 feet is required. Untouched natural habitat is best, but if designing a restoration project then plant native vegetation over as much of the property as possible to provide the best filtration. The best filter strip is mature woodlands with full ground-level, mid-story, and upper-story growth. The filter's effectiveness drops off as the amount of vegetation decreases. Full-height native prairie grasses along the shore are more effective as filters than short mowed lawns.
Erosion and Sediment
All disturbed soil should be stabilized. Sediment from soil erosion contains nutrients that promote excessive algae and bacteria in lakes. Stabilize and correct erosion problems as they occur by using one or more best management practice methods.
Carefully evaluate the need for turf grass near bodies of water. Over watering can waste valuable groundwater. Turf grass is poor at filtering out contaminants in runoff water before it enters a lake. Lawn fertilizers, pesticides, and herbicides have a tendency to reach the water systems and degrade them.
Keep gardens away from the water's edge. Use only safe additives and control any potential runoff from the exposed soil.
Storm Water Runoff
Try and reduce the amount of impervious surfaces. These "hard" surfaces that are impermeable to water decrease infiltration resulting in increasing the odds of flooding, erosion, and reduce the groundwater recharge. The City of Hastings has adopted a storm water ordinanceas well as a shoreland management ordinance addressing these issues.
Sewage Treatment
A properly run sewage system will prevent contaminants from leaking into the groundwater and surface waters. Inspect to make sure the system is working properly.
Toxic Chemicals
Avoid using toxic chemicals as much as possible. Dispose of hazardous household wastes at available turn-in sites. Use biodegradable soaps and household products. Carefully handle gasoline and motor oils especially when or near water.
Plant native grasses, trees, and plants. A healthy ecosystem with sensitive development, areas of native vegetation, natural shorelines, and good habitats for birds and wildlife provides a visually attractive environment.
For additional information visit the Department of Natural Resources.
In Minnesota, you can officially "adopt" a stretch of stream, river, or lake shore with the Department of Natural Resources. The program supplies free garbage bags, gloves, and recognition for volunteers. Information is provided as to what to do with tires and car batteries. For additional information visit Adopt-A-River


text:
Online Health Resources
The following health links are designed for educational and resource purposes only. They are not a substitute for professional care. If you have or suspect you may have a health problem, you should consult your health care provider or call the Brooklyn College Health Clinic at 718.951.5580. Please note that price ranges, where listed, may be subject to change.
- Abuse / Crisis Hotlines — Lifenet crisis intervention
- NYS Domestic Violence Coalition
- Lifeline Suicide Prevention
- New York State HOPEline
Alcohol and Drug Use
- Center for Disease Control (CDC)
- National Council for Alcohol and Drug Dependence
- Phoenix House
- Addiction Recovery
Complementary and Alternative Medicine
- Association of Reproductive Health Professionals (ARHP)
- ARHP Method Match
- Planned Parenthood
- Emergency Contraception
- Many individuals are eligible for free or low cost family planning
Digestive Health Information
Eating Disorders
- Food Addicts in Recovery Anonymous
- National Eating Disorders
- Overeater's Anonymous
- Health at Every Size
Environmental/Occupational Health
General Health Information
- American Academy of Family Physicians
- Centers for Disease Control http://www.cdc.gov/
- Go Ask Alice
- National Institute of Health
- NOAH: New York Online Access to Health
- MEDLINE Plus Health Information
Medication Information
Men's Health
Mental Health
- Center for Food Safety and Applied Nutrition
- Center for Science in the Public Interest
- Nutrition Data
Sexual Health
Smoking and Tobacco Use
- American Cancer Society
- American Heart Association
- American Lung Association
- Centers for Disease Control
- CUNY Tobacco Policy
- Legacy Foundation
- National Cancer Institute
- NY State Smoker's Quitline
- NYC Quits
- Smoke Free Text: For young adults and adults
- Cessation programs for staff: CUNY Work/Life Program; Employee Smoking Cessation Program (ESCAPE)
Women's Health
Brooklyn College Links
- Center for Student Disability Services
- Early Childhood Center
- Immunization Requirements/Health Programs Office
- Lactation room available for nursing mothers in Early Childhood Center, 1604 James Hall
- Personal Counseling
- Sexual Harassment Policy
- Speech and Hearing Center
- Student Affairs
- The Women's Center


text: As Time Goes By Series 9 - 5. You Must Remember This - Part One Share: About this programme 5/6. First of two specials featuring clips from past episodes. With Alistair and Judith now married, and Sandy and Harry in Canada, Lionel and Jean reminisce on their own. Cast and crew Cast Jean Pargetter Judi Dench Lionel Hardcastle Geoffrey Palmer Alistair Deacon Philip Bretherton Judith Hanson Moira Brooker Sandy Jenny Funnell Crew Director Sydney Lotterby Writer Bob Larbey Writer Colin Bostock Smith Categories Sitcom Share:


text:
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To find a Dorsey attorney, click the first letter of their last name or enter keywords in the search box and click the magnifying glass icon.
Click on the appropriate link for more information on a career at Dorsey.
Learn more about Dorsey including our pro bono programs, community involvement, management, firm history and more.
Global law firm Dorsey & Whitney is the recipient of the 2010 SAGE Best Practices award, given by the Legal Profession Committee of the Minnesota State Bar Association. The SAGE award recognizes the efforts of law firms in the recruiting, hiring and development of women attorneys. SAGE, which stands for "Self-Audit for Gender Equity," is an initiative of the Minnesota State Bar Association's Women in the Legal Profession Committee, which surveyed law firms, corporations and government entities to determine "best practices" for the hiring, retention, and promotion of women in the legal profession. An in-depth analysis of the survey results identified best practices in six key areas: equal access, workday issues, governance, evaluation/promotion, retention, and compensation. In each area, the Committee identified specific steps that employers could take. The SAGE Award winners are those employers who have implemented these practices and achieved measurable successes. Dorsey adopted the SAGE Best Practices in 2004 and since then has instituted a number of programs and policies to attract, develop and retain women attorneys. Dorsey Diversity Steering Committee Co-Chair Sarah Herman said: "Adopting these best practices has contributed to improved retention and promotion of women lawyers in the firm. Lawyers who might otherwise have left the firm have stayed and expressed strong satisfaction with their flexible work arrangements and the firm because of our flexibility."
Today, 51% of Dorsey's associates are women and 48% of senior associates are women. Of the 43 new Dorsey partners promoted in the past three years, 22 were women and a third have had flexible work arrangements during their Dorsey careers, including at the time of promotion.
The SAGE Award further cements Dorsey's reputation as a great place for women, as it follows recognition from Working Mother magazine, which, three years in a row, named Dorsey to its annual "Best Law Firms for Women" list. Dorsey also received the Alfred P. Sloan Award for Business Excellence in Workplace Flexibility.