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Search Tags: gifts
Obamas sort gifts in annual Toys for Tots drive
President Obama and first lady Michelle Obama helped sort donated gifts on Wednesday at Joint Base Anacostia-Bolling as part of the Marine Corps' Toys for Tots campaign.
Tags: Toys for Tots , Marine Corps , Joint Base Anacostia-Bolling , Barack Obama , Michelle Obama , Christmas , donations , White House
#whatstrending: How are you celebrating dad?
We fathers don't ask for much, though. Many of us don't even need the neckties that have become a Father's Day gift cliche.
Tags: What's Trending , Father's Dad , dad , #giftsfordad
Searching for Father's Day gifts? Here are some ideas
Is dad a grill master? Would you like him to be a mix master? Here are some gift ideas and some sites offering free shipping.
Tags: Father's day , food , grilling , iPad , Colleen Kelleher
Mad shopping dash: Stating the obvious -- expect crowds
As you hustle about to get those last-minute deals, let others know where you are finding them. Tweet your find using #WTOPdeals or tell everyone about it on WTOP's Facebook page.
Tags: Christmas , Shopping , retail , Andrew Mollenbeck , Colleen Kelleher
Regifting? 8 tips on what not to do
Follow these etiquette rules and you should be able to avoid embarrassment.
Tags: regift , Christmas , regifting , Shopping , retail , Colleen Kelleher
How free is Free Shipping Day?
Is it a real money-saver or a real gimmick? What you might not know about Free Shipping Day.
Tags: free shipping day , holiday shopping , Nathan Hager , wtop saves you money
RATTS crawling around area retailers
RATTS, or Retail Anti-Theft Teams, have recovered thousands of dollars worth of stolen cologne, perfume, clothing, iPhones and much more from organized bands of out-of-state thieves who are targeting area stores on both sides of the Potomac River.
Tags: Retail Anti-Theft Teams , RATT , Fairfax County Police , Lucy Caldwell , Jay Carson , iPhone , Shopping , Christmas , Street Vendors
Merry Christmas: Here's a bra
What's the most unusual Christmas gift you've received from a co-worker?
Tags: Christmas , bra , Shopping , Colleen Kelleher
5 fresh and simple gifts to make by hand
If you're inclined toward making your own gifts this holiday season, look in on the following five ideas, which stand out for their simplicity and freshness.
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Electronics Top Holiday Gift Purchases
Though Americans are still tightening their belts when it comes to spending, it won't stop them from purchasing this year's most popular gifts: electronics.
Tags: holiday hires , electronics , Consumer Reports , Trouble Tracker Index , Amanda Walker , holiday shopping , Christmas shopping


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|FastFloors.com » Hardwood Flooring » Columbia » Amelia Hand Sculpted Engineered 5 » Candlelight Ash
|About the Product - Columbia Amelia Hand Sculpted Engineered 5
Columbia Amelia Hand Sculpted Engineered 5 Hardwood Flooring shown in Candlelight Ash with coverage 22.5 s/ft, sold by box, length random, width 5 inch, thickness 0.5 inch, color beige, construction engineered, design plank, finish aluminum oxide, species ash, janka hardness 1320, surface matte, scalloped - sculpted edge, scalloped-sculpted end, glue together install, locking floor installed without glue, glue down install, nail down install, staple down install, 25 year warranty.
Heavily Sculpted
Light undulating bevel along edge

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Featured Frame Wednesdays: April Showers Bring... Pantone Fashion Colors to 10/10 Optics!
Last updated 1 year ago
Sure, the weather has been wet and gray lately, but at least it's not the Polar Vortex! What better way to celebrate spring and add a splash of color to your world than with new frames in Pantone Fashion Colors from 10/10 Optics?
This year's Pantone palette features hues from elegant neutrals to soakingly saturated corals and blues, and boldly pairs them for looks that span the spectrum from funky to romantic - and back again!
Stop by 10/10 Optics to consult with our Eyewear Makeover experts and find the unique color combo that will capture your personality, make your style pop - and your friends' jaws drop!
Spring and Summer colors are in bloom at 10/10 Optics!


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April 2011 birth club, I have a huge favor to ask. My son is in a pretty big photo contest and we eed all the likes we can get. Will you please like the photo in the link below and, if you really like it feel free to share on facebook. <3
Thank you so so much.


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ARBORS AT HONEYCREEK Apartments200 W Wheeler Ave, Terre Haute, IN 47802 View On Map (812) 242-2563
For rates, availability, and other information fill out the availability request form and a representative from ARBORS AT HONEYCREEK Apartments will contact you shortly
Arbors at Honeycreek is located in the Terre Haute Area in the 47802 zip code. View this apartment on a map.
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Brief look at ups, downs of 2013
December 20, 2013 As the end of the year draws near it's always a good time to reflect on what has occurred over the past 12 months. more »»
Hunting set to give way to ice fishing
December 13, 2013 Hopes were running high going into the muzzleloading season that I might still get lucky enough to drop a buck. more »»
December 6, 2013 MARQUETTE — Aaron Peterson had no idea what he was getting himself into, but that's in a good wa. more »»
U.P. deer hunting not over just yet
December 6, 2013 The second half of the firearm deer season played out a lot like the first half — unpredictable, varied and challenging. more »»
Former NMU student tries for 'big year'
November 29, 2013 MARQUETTE — What do Michigan and Hawaiian birds have in common? Other than feathers, an avid birder who is concerned about avian conservation in both places. more »»
Poaching problem highlighed with black bear shooting
November 29, 2013 LANSING — A bear-poaching incident in Manistee County has shed light on a continuing poaching problem throughout Michigan. "A poacher is nothing more than a criminal. more »»
Feds award Great Lakes area urban forest grants
November 29, 2013 TRAVERSE CITY (AP) — The U.S. Forest Service is awarding nearly $2. more »»
Law stiffens fines for illegal killing of deer
November 29, 2013 LANSING (AP) — Michigan Gov. Rick Snyder has signed a law stiffening penalties for illegally killing or selling antlered white-tailed deer. more »»
Wild turkeys once again plentiful in Michigan
November 29, 2013 LANSING (AP) — The wild turkey has completed its comeback in Michigan, but not all are thrilled with the development. more »»
Platforms provide loon nesting opportunities
November 22, 2013 MUNISING — People might pose the greatest threat to loons, but some local youths are trying to lessen that threat. more »»
Deer season looking a lot like expected
November 22, 2013 It appears pre-season predictions for the firearm deer season were pretty accurat. more »»
Gone fishin'
November 22, 2013 Upper Peninsula Little Bay De Noc: Few anglers have been out but those targeting walleye at night have caught fish from the reefs near Kiplin. more »»
Lace 'em up at snowshoeing workshop
November 15, 2013 NEGAUNEE — Unless they're a snowshoe hare, most people don't have oversized feet that will carry them across deep snow without sinking up to their waists. more »»
Opening day has double meaning
November 15, 2013 The day has finally arrived and thousands of deer hunters are now sitting in their blinds, walking that favorite ridge, creeping through a swamp or watching over a scrape line — all having visions o... more »»
Gone fishin'
November 15, 2013 Upper Peninsula Menominee River: With one gate open at the Hattie Street Dam, anglers were catching walleye and whitefish when using jigs with crawlers or minnows in the current. more »»
Area dune restoration has multiple benefits
November 8, 2013 HARVEY — Dune grasses can do more than improve a habitat. They can help make snowy highways a lot more passable. more »»
Deer hunting's appeal seen in old magazine
November 8, 2013 Digging through a basement storage room in search of a misplaced item in the house that I've lived in for nearly 27 years can be an interesting endeavo. more »»
Gone fishin'
November 8, 2013 Upper Peninsula Fall's River: Is producing some steelhead. Try drifting crawlers and spawn or casting small spoons and spinners. more »»
Chocolay Raptor Center educates, entertains
November 1, 2013 HARVEY — Raptors, or birds of prey, have an important ecological niche. On top of the food chain, they keep the populations of prey like mice in check. more »»
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What to expect from a survey
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Re: What to expect from a survey
Generally a good surveyor will inspect structural details, look for evidence of leaks (both hull related and deck/portlight related), evidence of good care and maintenance, visual/cursory inspection of deck level rigging, inventory of required equipment, esp safety compliance issues (fire extinguishers up to date and present, etc.). They will recommend priority items to be addressed/repaired and often 'nice to do' but not urgent items too. You'll get a written report, nowadays with photos. This report will be used by you to evaluate the deal and possibly renegotiate based on flaws/issues found, it will also be needed by any financing institution and insurance provider.
It's very important to be present and you'll glean a lot of info as the survey progresses. Try to book a seatrial on the same day and take the surveyor along.. If the surveyor doesn't want you there, look for another one. Few surveyors will actually, properly inspect rigging or machinery like engines and generators. Many buyers commission such inspections by separate experts.
Expect to pay between $10-20 per foot OAL.. with probably 10-12 the average. Tough to say if you'll get a 'better' survey from a more expensive one.. try to get some recommendations from locals (not the seller's broker)
Good luck!
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Global air traffic grows 3.2 percent in April—IATA
CAPE TOWN—Global air-passenger traffic grew by 3.2 percent in April, compared to a year ago, driven by travel in emerging markets, the global airline industry body said on Thursday.
"We see strong growth among African, Asia-Pacific, Middle Eastern and Latin American airlines," International Air Transport Association chief Tony Tyler told journalists in Cape Town.
Middle Eastern airlines were the strongest performers, growing by 10 percent, followed by Asia-Pacific at 4.7 percent and Africa at 4.6 percent. Latin America grew by 2.8 percent.
Growth was more modest in Europe where demand increased by 2.2 percent. The North American market rose 0.6 percent.
The seasonally adjusted rate for April was nearly five percent, the airline industry body said.
Marginally ahead of demand, capacity was up 4.4 percent from last April, pushing the load factor downward by 0.9 percent to 78.1 percent.
If adjusted for seasonality, this remained at near record highs of 80 percent.
The international flight market was up 3 percent in April from the year before, while capacity grew 4.3 percent.
North America was the only region to contract, shrinking by 0.5 percent, while the Middle East topped international demand with a rise of 10.9 percent.
Africa was the second-best performing region with traffic rising by 4.7 percent from the figure for April last year.
Domestic demand was up 3.5 percent, fueled by China, with other markets shrinking apart from Australia.
IATA will hold its annual AGM next week in Cape Town.
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Digitimes Research: Taiwan 2Q13 IC design industry output value rises 21.5%
Nobunaga Chai, DIGITIMES Research, Taipei [Friday 30 August 2013]
Taiwan's IC design industry registered growth of 21.5% sequentially and 13% on year in output value in the second quarter of 2013, thanks to growing demand coming from China's smartphone and tablet sectors, according to Digitimes Research.
Taiwan's IC design industry generated combined revenues of NT$115.98 billion (US$3.88 billion) in the second quarter of 2013, said Digitimes Research. Combined revenues of Taiwan's top-10 IC design houses, including companies specializing in analog ICs, consumer ICs and niche-market memory chips, came to NT$75.52 billion in the quarter, up 23.2% from the prior quarter and 22.9% a year ago.
Orise Technology among Taiwan-based IC designers enjoyed the largest sequential growth in revenues in the second quarter, buoyed by rising shipments of LCD driver ICs for use in smartphones shipped to China, Digitimes Research indicated. Orise generated revenues of NT$2.93 billion in the second quarter, up 48.8% on quarter.
MediaTek remained the largest Taiwan-based fabless IC company in the second quarter of 2013, with revenues rising 38.8% sequentially to NT$33.28 billion, Digitimes Research disclosed.
This article is an excerpt from a Chinese Digitimes Research report. Click here if you are interested in receiving more information about the content and price of a translated version of the full report.
Source: Digitimes Research, August 2013
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Computex Taipei 2015
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The Gator Ticket Office announced on Tuesday that fans will have a new opportunity for securing 2014 Florida football tickets. Beginning July 1 (at 8:30 a.m.), fans will have the option to purchase a three-game mini-plan that includes either the LSU or South Carolina home game with two additional games.
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I am no poet nor do I want to write a novel.
I am interested in the multiplicity of language.
My tendencies tend to focus on Experimental & Conceptual Writing.
I also translate from my native Spanish into English and vice versa.
Whatever I post are mere explorations on upcoming works or ideas
I am obsessed with Heidegger, Derrida, Lorca, Ponge, Vanessa Place, among others.
Currently working on a conceptual manuscript exploring erasure as applied to translations of Latin American poets like Eduardo Milán.
I like grapefruit.


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Lipstick Addict, Horror Junky, and Purple -Haired Vegan. Sometimes I blog, mostly I don't.
Collecto is the most complete web platform to enjoy your Instagram photos. Collecto uses the Instagram API but is not endorsed or certified by Instagram.
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PHOTOS: Crime Scene in the Bronx
Sources have confirmed that the dismembered body found scattered along several streets in Melrose is Tanya Byrd. The body parts were found stuffed into bags at around 4:30 a.m. in the area around Eagle and Cauldwell avenues, and East 156th and 158th streets.
Way to Go kids Tommy Bahama, Aveda, more new stores on LI See LI landmarks by air LI veterans then and now Nassau Class B final: Manhasset vs. Lynbrook Suffolk girls track county championships Suffolk Class B final: Hauppauge vs. Eastport-South Manor Nassau Class A final: Massapequa vs. Syosset Suffolk Class C final: Mt. Sinai vs. Bayport-Blue Point Suffolk boys tennis team championships: Hills East vs. Hills West Suffolk Class A semifinals: SWR vs. Glenn Suffolk Class AA winners bracket final: Patchogue-Medford vs. Sachem East


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BABY BLUE HUBBARD *ORGANIC$3.4595 DAYS A small version of Blue Hubbard. High yielding plants that produce 7-10 lb. baby hubbards. Fruits have sweet, orange flesh. Learn More
BLACK BEAUTY Zucchini- Organic$2.9550 days
Blocky, glossy black-green fruits. Early and productive with firm, creamy white flesh. Fruits are 6-8 inches long and 2 inches in diameter.
Additional Infomation:
Open Pollinated Learn More
COCOZELLE SQUASH$2.9542 days
Popular Italian variety with striped fruits. Quick maturing and flavorful. The fruits are 10-12 inches long with very firm greenish-white flesh.
Additional Infomation:
Heirloom variety Learn More
DARK GREEN ZUCCHINI SQUASH- Organic$2.9545 days
Produces small uniform fruit of high quality usually found only in hybrids. Dark green with light flecks, good production, and an authentic zucchini flavor. Very high yields. Learn More
DELICATA SQUASH *Organic$3.7595 days
Specially selected uniform strain! Oblong striped fruits, 7-9 inches long and 3 inch diameter. Delicious, fine grained, orange-yellow flesh. Tastes better than Butternut! One of our best keepers. Learn More
EARLY BUTTERNUT SQUASH - Organic$3.4580 days.
This is the only northern-friendly butternut. The 10-12 inch buff fruits are uniform and the semi-bush plants are quite productive. Good, sweet butternut squash flavor.
Additional Infomation:
All American Selection Learn More
EARLY PROLIFIC STRAIGHTNECK SQUASH - Organic$2.7542 days.
Lemon yellow; like crookneck except straight. Best eaten when 4-7 inches long. An abundant producer of high quality fruits with a firm, fine grained flesh.
Additional Infomation:
All American Selection
Open Pollinated Learn More
EARLY SUMMER YELLOW CROOKNECK SQUASH - Organic$2.9542 days
Semi-open plant produces smooth yellow fruits that mature orange and warted. The best summer squash for buttery, rich flavor and tender, but firm texture. Pick when 5-6 inches long. Produces all season long. Learn More
GOLD NUGGET SQUASH*ORGANIC$2.9575 days
Early, family-sized (1-3 lbs) squash, orange-red with faint salmon-colored stripes. Sweet orange flesh is thick and fiberless. Yields about 4 fruit-per-bush type plant. Perfect for small spaces. A good keeper. Bred for northern gardeners at the North Dakota Experiment Station.
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All American Selection Learn More
Hopi Orange Squash$44.00Organic. 90-100 Days. This pumpkin-shaped squash grows on vigorous vines and can range anywhere from 7 to 20 pounds. Excellent flavor for pies and soups. Learn More


text: I realized after my diagnosis four years ago that having bipolar disorder does not define who I am as a person. For close to three decades, I battled an unknown adversary, believing I was just sinning because I wasn't choosing happiness. The story is composed of trials from my life, including a turbulent relationship; hopelessness and thoughts of suicide; the abandonment of my Olympic dream; the death of a son in my arms from a genetic disorder; the early end to my teaching career; and the suicide of my nephew. I can now see how my undiagnosed bipolar disorder affected my decision-making ability, my relationships, my joy, and the goals I once had for my life. It is a story of triumph in the face of adversity, one that tells how God has given me a meaningful life even though things haven't turned out how I originally envisioned.


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Biotechnology Schools in Boston, MA with Degree Information
There are at least seven schools within 15 miles of Boston that have biotechnology degree programs. Read an overview of three schools' programs, requirements and admission info, and find out which school is the right one for you.
Boston, MA, Biotechnology School Options
Boston and its surrounding cities offer several schools for students looking to study biotechnology. All schools offering such programs in the city limits are private institutions except for one 2-year community college. This article looks at three schools within 10 minutes of downtown Boston. It also contains a table that offers a quick way to compare various aspects of the schools, such as school type, size and costs. Also included at the end of the article is a little information on a few more schools with biotechnology programs in the Boston area.
- The largest of the private institutions is Boston University, where students can enroll in a dual bachelor's/master's program to learn about biotechnology. Boston University is about two miles from downtown.
- Another private school, Northeastern University, also two miles from the city center, has a professional master's program in biotechnology with three concentration options.
- Individuals who want to earn a degree while avoiding the higher tuition rates of private universities may consider the 2-year associate's program at Bunker Hill Community College. Bunker Hill is about three miles from downtown Boston.
Boston University
BU offers more than 250 programs across 16 schools and colleges. The College of Arts and Sciences contains 24 departments. Faculty from the biology, chemistry and physics departments work together to offer the interdisciplinary dual-degree program.
Bachelor of Arts in Biochemistry and Molecular Biology/Master of Arts in Biotechnology
In the bachelor's program, students gain a foundation in various sciences like molecular biology, physics, biochemistry and biology. Students must also complete calculus and computer science courses and participate in laboratory research. During the junior year of the bachelor's program, students can apply to the Master of Arts in Biotechnology program. An additional six classes are required in the master's program as well as two semesters of guided graduate-level research and a research report. It typically takes five years to complete both degree programs. The M.A. in Biotechnology is not offered separately.
Northeastern University
The College of Science contains this school's biotechnology programs as well as programs in fields like biology, biochemistry, linguistics, math and physics. The professional master's program integrates business concepts into the science-based curriculum. A thesis isn't typically required in a professional master's program.
Professional Master of Science in Biotechnology
Students can only enter this program during the fall semester. Admission requirements include having a bachelor's degree in a relevant science or engineering field and undergraduate coursework in calculus, organic chemistry, biochemistry and either molecular biology, genetics or physiology. Students can choose from three concentrations: biopharmaceutical analytical sciences, pharmaceutical technologies and process sciences. All tracks have common courses that include biotechnology basics, molecular cell biology, biotechnology ventures and biotechnology lab.
Bunker Hill Community College (BHCC)
BHCC awards Associate of Arts, Associate of Science and certificate programs. The biotechnology program is one of more than 100 programs offered at BHCC.
Associate in Science in Biological Sciences - Biotechnology
In addition to course lectures, students in this program complete several labs. Some of the required courses include biology, chemistry, genetics, cell biology and molecular biotechnology. Students also choose 3 electives, which may include microbiology, statistics, calculus and organic biochemistry.
Comparison of Schools
There are many factors to consider when choosing a school. The following table offers a side-by-side look at the three schools featured in this article, making it easy for students to compare them.
|Boston University
|Northeastern University
|Bunker Hill Community College
|School Type
|Private, non-profit, 4-year university
|Private, non-profit, 4-year university
|Public, 2-year community college
|Total Enrollment (2011)
|Campus Setting
|Large city
|Large city
|Large city
|Undergraduate Tuition and Fees (2012-2013)
|$3,384 in-state; $8,328 out-of-state*
|% of First-Year Students Receiving Any Form of Aid (2010-2011)
|Acceptance Rate
|49% (fall 2011)*
|32% (fall 2012)*
|N/A - Open admissions
|Retention Rate (2010-2011)
|92% full-time students*
|95% full-time students*
|73% full-time students; 69% part-time students*
|Undergraduate Graduation Rate
|85% (students who began in fall 2005)*
|77% (students who began in fall 2005)*
|9% (29% transfer-out rate) (students who began in fall 2008)*
Source: *NCES College Navigator
Other School Options
Also in Boston, Suffolk University has a Bachelor of Arts/Bachelor of Science in Biotechnology program. Less than 4 miles outside of downtown Boston in Roxbury Crossing, Roxbury Community College has a certificate program in biotechnology/biomanufacturing and an associate's degree in biotechnology. Tufts University, about 7 miles away in Medford, has undergraduate and graduate programs in biotechnology. Lastly, an associate's degree program in biotechnology and compliance is offered 13 miles outside of Boston at Quincy College.
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Picture: John Travolta, Kelly Preston, and daughter Los Angeles Premiere of 'Hairspray' held at the Mann Village Theatre Westwood, California - 10.07.07
John Travolta was honoured in New York last night (05DEC04) when pals OPRAH WINFREY and Kirstie Alley teamed up to toast the movie star for his huge heart.
The Pulp Fiction actor brought out the stars at the Waldorf Astoria Hotel, where he was honoured by New York's Museum of the Moving Image.
Winfrey hosted the black tie affair, and used the evening to pour praise on her friend. She said, "An opportunity to declare my love for John Travolta? Oh yeah, I had to be here."
His LOOK WHO'S TALKING co-star Alley added, "I feel like John is my soulmate."
Kathy Bates and James Gandolfini, who co-starred with Travolta in PRIMARY COLORS and Get Shorty respectively, were also present at the gala, as was his actress wife Kelly Preston.
Travolta became the Museum's 20th honouree and joins the likes of RICHARD GERE, Billy Crystal, Mel Gibson, Julia Roberts and Tom Hanks.07/12/2004 00:02


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DC39 Multi Floor has Dyson's latest Radial Root Cyclone technology, with airflows remodeled to maximize suction power. It has Ball technology to turn on the spot and follow with greater control. DC39 Multi Floor comes with a Triggerhead tool that adjusts at the handle for different floor types – no need to bend down.
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By Lynnem
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Comments about Dyson DC39 Multi Floor Canister Vacuum:
So far I love my new vacuum the only problem is the switch to change from rugs to hard surface. I called and it was replaced quickly with no issues but the replacement has the same problem. I hope that when the issue is resolved there will be a new and improved switch will be sent out to all registered owners. Just want to say again with 2 dogs my house is much cleaner now
Bottom Line Yes, I would recommend this to a friend
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By Mommaof4
The power is excellent but not sure why they put a ball on the canister. It still gets stuck on furniture as I'm vacuuming it's e front wheels that get stuck so it defeats the purpose of having the ball. Also, the filter that needs to be cleaned once a moth take about 2 days to dry and I vacuum daily so I was having to wait for it today to get to vacuum which was a bit aggravating
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Over all the DC39 has the ...
By DianeK
Over all the DC39 has the same strength and cleaning power of the upright vacuum that I replaced. I find that after emptying the canister, it takes me a couple of times before I get it to go back/ fit into the right spot.
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love my dyson
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My Dyson DC39
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Great Vacuum!
By ErinL
The Dyson DC39 vacuum is GREAT! After using it the first time and seeing what dust and dirt it picked up, it made me feel like my other vacuum only moved the dirt around. It is so easy to vacuum our stairs and underneath furniture. However, it does have a bit of trouble picking up big items such as goldfish but that something easy enough to pick up by hand. I love this vacuum and will definitely stick with the Dyson brand!
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Partner: Dallas Museum of Art
Serial/Series Title: The Past in Review -- No. 1
- The Art That Broke the Looking-Glass
- Catalog from the exhibition, "The Art That Broke the Looking Glass," November 15–December 31, 1961, held at the Dallas Museum for Contemporary Arts. Includes: list of artists and artworks in the exhibition, essay, images. texashistory.unt.edu/ark:/67531/metapth176578/


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Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking.
By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs.
IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at http://hanlab.genetics.ac.cn/IntNetDB.htm webcite. Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer.
Protein-protein interactions (PPIs) underlie most biological processes. Dissecting the PPI network for a particular biological process may provide important clues into molecular mechanisms of the process [1]. Recently, large-scale experimental studies have generated many PPI datasets in different model organisms by yeast two-hybrid (Y2H) screens [2-8] and by co-affinity purification (co-AP) followed by mass spectrometry (MS) [9,10]. These studies have provided opportunities to examine cellular function at a network level.
There are two shortcomings of these data: (a) the coverage is very low and far from complete, and (b) the accuracy of each dataset is generally not very high and varies considerably from dataset to dataset [11]. The unreliability and incompleteness of PPI data complicates elucidation of biological processes or cellular functions, and may potentially misrepresent the topological features of the network [12]. Many methods have been used to predict PPI networks [13]. These fit into three categories: sequence based [14], high-throughput data-based, and a combination of sequence and high-throughput data. The sequence-based prediction methods include gene fusion, gene neighborhood and phylogenic profiles [15], and predictions based on protein/domain structure [16,17]. The high-throughput data based methods predict PPIs from data generated by high-throughput experiments, such as correlated mRNA expression [11,18], correlated phenotype profiles [19], shared protein interaction partners [20], shared genetic interaction profiles [21,22], or similar subcellular localizations [17]. The combination methods predict interologs based on gene orthologs [23,24].
Recently machine learning methods have been introduced to predict PPIs by combining genomic and experimental features. Bayesian classifiers are probability-based and competent in integrating large numbers of heterogeneous datasets [25-27]. Probabilistic decision trees and random forest (a collection of decision trees) specialize in classifying objects into different categories [28-31]. Logistic regression is especially suited for assigning elements into two opposing groups [32-35]. Support vector machines (SVM) have been used to predict PPIs from a limited number of attributes to binary outputs (interact versus not interact), but has not been used for integrating multiple evidences [36-43].
Among these machine learning approaches, Bayesian probabilistic model has many unique advantages in predicting PPIs. It can handle heterogeneous data types, such as numerical phenotype values, discrete survival fitness values, vector microarray expression values, binary interactome values or categorical Gene Ontology annotation values. Heterogeneous data types can be transformed into one uniform probabilistic score by calculating the likelihood ratios. Each data source is automatically weighted according to its confidence level. Missing data are tolerable for integration. Furthermore, Bayesian model is a fast simple algorithm, as it is probability-based and does not require much time to standardize different data of different sources or types. Most importantly, Bayesian model has been proven by previous studies to be particularly competent in predicting PPIs [31,32]. Lastly, the simple integration scheme is very suitable for updating or including future datasets.
To date the Bayesian model has mostly been applied to yeast, and rarely to predict human PPI [27,44]. Rhodes et al integrated 13 datasets of four different data types: physical interactions in model organism, co-expression, domain-domain interactions and shared biological functions [27]. However, other types of high-throughput data then available were not examined. Since the publication of this analysis many other high-throughput data have been generated, some directly done on human proteins. Furthermore, the ever-growing high-throughput data and the data mining demand from the research community require a more comprehensive, current and updatable integration platform and database for integrating, storing, visualizing and mining the data. Toward achieving these goals, we examined the predictive power of new data types and datasets, created an Integrated Network Database (IntNetDB) and provided easy-to-use web-based visualization and data mining options.
We chose to adopt the Bayesian analysis method, because of unique advantages in predicting PPIs [45], and because of its proven effectiveness established by previous studies [25-27]. From the first [25] to the latest study [27] using this analysis framework, more accurate and more extensive integrated PPI networks have been predicted. Here, using ten-fold cross validation, we also demonstrated the effectiveness of Bayesian analysis in predicting human PPIs from 27 datasets of seven different data types.
To allow researchers easy and rapid use of our prediction results, we assembled the data in a web-accessible Integrated Network Database, and we provide a graphic-user-interface for querying PPIs among a group of query proteins/genes (Figure 1). We also provide an online tool for creating customizable visualization of the network, and a computational method to search for highly-connected network modules, as these modules frequently correspond to molecular machines [19]. We used the MCODE algorithm with default settings to find network modules based on network topology. The details about the algorithm and the principles are in the original paper [46]. Furthermore, the design of the database and user interface allows easy incorporation of new datasets.
Figure 1. A brief description of the integration by the probabilistic model. Seven heterogeneous dataset types are gathered and evaluated by the gold standard positive (GSP, all the annotated protein-protein interactions from HPRD) and gold standard negative (GSN, possible protein pairs between the proteins on the plasma membrane and those in the nucleus). The potential of forming a protein-protein interaction is scored as the likelihood ratio (LR) for protein pairs to be true positive interactions versus true negative interactions, according to the GSP and GSN. Each interaction is assigned a LR within a data type. When evidence arises from more than one datasets within a data type, the maximal LR among the datasets is used for a gene pair. Then the LRs given by different data types are integrated by the Naïve Bayes model, which generates the final prediction score for a potential PPI by multiplying all the LRs from the seven distinct data types. Lastly, the final integrated network with an acceptable confidence level for each interaction is presented.
Construction and content
Gold standard for integration
Naïve Bayes classifiers require a gold standard positive (GSP) and a gold standard negative (GSN) dataset. The Human Protein Reference Database (HPRD) [47] is a protein-protein interaction database with 19,438 distinct interactions among 5,983 proteins. It is manually curated by expert biologists based on small-scale and focused experiments described in the scientific literature. We accepted it as high quality and used it as the GSP dataset. We used the GSN dataset previously generated by Rhodes et al [27], which includes all the possible pair-wise combinations between two sets of proteins that are assigned a subcellular localization of the plasma membrane (1397 proteins) and the nucleus (2224 proteins), respectively, by the Gene Ontology (GO) Consortium [48]. The GSN includes a total of 3,106,928 interactions. The size and the ratio of GSP and GSN are adequate for covering interactions of low prediction probability and for predicting human PPIs [27]. To measure the predictive power or confidence level, we used the likelihood ratio (LR) of a gene pair to be a true positive interaction versus a true negative. This is calculated by Pr(E|GSP)/Pr(E|GSN), where the Pr(E|GSP) is the probability of a certain evidence observed within GSP set and Pr (E|GSN) is the probability of a certain evidence observed within GSN set (Methods).
Physical protein-protein interactions
As PPIs are frequently conserved through evolution [49,50], we gathered the high-throughput experimental PPI data of the model organisms Saccharomyces cerevisiae [2,3,9,10,51] (SC1-5, yeast interactome dataset 1–5 [2,3,9,10,51]), Caenorhabditis elegans [5] (CE), and Drosophila melanogaster [4,6] (DM1 and DM2, fly interactome dataset 1 and 2 [4,6]). We mapped interactome data from these different model organisms to human through protein orthologs determined by best reciprocal BLASTP hit (Methods). The confidence for each of the datasets was evaluated. Whenever confidence level information is available in the original study, we divided the dataset into different groups according to their confidence levels (Figure 2A). The results indicate that interologs (ortholog pair) derived by these methods generally have strong predictive power. Among them the S. cerevisiae interactome dataset 4 [10] is the most predictive (LR = 1438.9). We also evaluated the recently published human interactome [7,8] (HS1 and HS2, human interactome dataset 1 [7] and dataset 2 [8]) and calculated the LR for those two datasets (LR1 = 11.1 and LR2 = 112.5). In brief, all the physical interaction datasets examined can be incorporated in the integration (Figure 2). The big differences in predictive power of different datasets (even those generated by the same method over the same proteome) point to the necessity of data annotation and integration before utilizing the data to derive biological hypotheses.
Figure 2. Assessing the performance of each dataset in predicting the human protein-protein interactions. A. Large-scale protein-protein interaction (PPI) datasets from model organisms and human. The datasets SC5 and DM1 are binned by their confidence level given by the original studies: LC, low confidence; HC, high confidence. B-D. Phenotypic datasets from model organisms. The fly genome-wide RNAi dataset is evaluated by the arithmetic difference in phenotypic values between a pair of genes (D). The phenotypic similarity of yeast genes upon knock-out is evaluated by cosine distance for the discrete values (C) and PCC for the continuous values (D). E. Yeast genetic interaction datasets. Yeast genetic interactions are grouped by the number of shared neighbors between a pair of genetically interacting genes. F. Large-scale human gene expression datasets. Gene pairs are binned by their Pearson Correlation Coefficient (PCC) between the expression profiles of the pair. The purple, yellow and blue curves are derived from three different expression datasets [57-59]. G. Domain-domain interaction (DDI) score. The DDI score of a domain pair is assigned to a pair of proteins containing the domains. If different scores exist between a pair of proteins arising from different interacting domain pairs, the maximum of the scores is assigned to the pair. The protein pairs are grouped according to their DDI scores. H. Smallest number of shared biological processes (SSBP) of yeast (SC), worm (CE), fruitfly (DM), mouse (MM) and human GO annotations. Gene pairs are binned by the smallest number of shared GO annotations between a pair of genes. Then the LR of being GSP versus GSN is calculated and plotted for gene pairs within each bin for each organism. I. Gene context analysis to predict PPIs. Three types of in silico prediction results are evaluated (gene fusion, gene co-occurrence and gene neighborhood).
Phenotypic data from model organisms
Loss of function among interacting or functionally related proteins tends to result in similar phenotypes [52-54]. Several large-scale phenotypic datasets are available for model organisms [52-54]. RNAi phenotype data have been used to predict PPIs for model organisms [19]. To examine whether phenotype data from model organisms are also predictive for human PPIs, we transferred phenotype data from model organisms to human by matching the genes in model organisms to their corresponding human orthologs. We then calculated the pair-wise phenotype similarity scores between genes. Considering the various forms of the phenotypic data, we used different measurements for phenotypic distance depending on the form of phenotypic values. For the dataset with one value for each gene under a single condition, we simply used the absolute value of arithmetic difference between the phenotypic values [52]. For the dataset with discrete values under multiple conditions, we used the cosine value between the phenotypic values of a pair of genes [54]. For the dataset with continuous value under multiple conditions [53], the Pearson Correlation Coefficient (PCC) was used. Then each phenotypic dataset was binned according to the similarity score (phenotypic difference, cosine or PCC) and the LRs were evaluated within each bin. A correlation between phenotypic similarity and LR can be observed even in the cross-species phenotypic datasets (Figure 2B–D). Therefore phenotype data can also be integrated to predict the PPI network.
Genetic interactions from model organisms
Synthetic genetic analysis (SGA) has been used in Saccharomyces cerevisiae [21,22] to globally map yeast genetic interactions. A significant overlap between PPIs and genetic interactions was recently demonstrated [55]. The number of common neighbors between a pair of genetically interacting genes can be used to predict potential PPIs [22]. We implemented such an analysis on binary yeast genetic interaction datasets [21,22]. First, the genetic interactions were mapped as human interologs. Then we binned all the interactions by the number of the shared neighbors, and the LRs were calculated for each bin (Figure 2E). We found that the more neighbors a pair of genetically interacting genes share, the more likely a direct PPI occurs between them. These genetic interaction data actually gave rise to very high confidence PPI predictions, slightly lower than large-scale PPI mapping (Figure 2E).
Gene co-expression
Genes that exhibit mRNA co-expression tend to show protein interaction [56], especially for those in the same complex or in the same biochemical reaction. Such correlated genes might be regulated by the same transcriptional factor or a set of transcription factors. We examined three high quality large-scale microarray datasets [57-59] to predict the human PPIs. One dataset consists of gene expression profiles during aging of the human brain, and the other two consist of gene expression profiles across a variety of tissues or cell lines. For each dataset, gene pairs were assigned into 20 bins of increasing pairwise expression PCC values (Figure 2F). We observed a significant correlation between expression PCC and the likelihood of forming direct PPI (measured by LR) between a pair of genes when the PCC is above 0.5, which may help to predict human PPIs (Figure 2F).
Shared functional annotation
Proteins with the same biological function are more likely to physically interact than those without. In addition, proteins sharing a more specific annotation are more likely to interact than those sharing a commoner less specific annotation. The Gene Ontology Consortium [48] has assigned 4,416 GO annotations to 14,801 human genes (proteins). To quantify the similarity between gene annotations, we identified the smallest shared biological process (SSBP) between a pair of genes [27]. The SSBP is calculated by three steps: (1) find all the GO terms shared by each pair of genes, (2) find the number of other genes also sharing these GO terms, (3) get the GO term with the smallest gene count. In agreement with expectation, the smaller the SSBP count, the more likely the proteins are to directly interact (Figure 2G). We also examined the GO annotations of four model organisms (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Mus musculus). When genes from the four model organisms were mapped to human orthologs, the SSBP still has good predictive power to predict human PPIs (Figure 2G).
Domain-domain interaction (DDI)
Many protein-protein interactions are mediated by domain-domain interactions. If two domains can physically interact, proteins containing these two domains are also likely to interact. DDI predictions have been explored before. We used the domain-domain interaction scores in InterDom, which are derived largely from structural information [16]. After transferring the scores to each pair of proteins that contain the interacting domains, we observed a weak but still clear correlation of the domain interaction score with LR (Figure 2H).
Gene context analysis
Gene context refers to in silico PPI predictions based on genome sequences [11]. Three types of gene context information have been used to predict protein-protein interactions: a) gene fusion/fission, which finds that interacting proteins in one species are more likely to be fused into one single protein in another species; b) gene co-occurrence which finds that interacting proteins are more likely to be found both present or both absent in an organism with a fully sequenced genome; c) gene neighborhood which finds that functionally coupled genes (interacting proteins or proteins in a complex) are more likely to be located in the same operon or gene cluster in a genome. Gene context analysis has been previously performed by von Mering et al, [11] to generate in silico PPIs for Saccharomyces cerevisiae. We mapped these interactions to human through interologs and found that all three types of in silico PPI predictions are suitable for predicting human PPIs (Figure 2I).
Integration by probabilistic model
We chose the Naïve Bayes classifier model because it integrates all the independent evidences simply by multiplying the LRs of diverse evidences [25]. Such expediency will facilitate integration of more datasets in the future. Using such an integration scheme, many weak evidences from several data types can be accumulated to predict interactions with increased confidence. We assume that different data types are conditionally independent, because they are derived from different experimental technologies that aim to measure different biological features of genes/proteins or protein pairs, or are from unrelated annotations. We categorized the 27 datasets into seven different types (Figure 1) to satisfy the requirement of conditional independency by the Naïve Bayes classifier. The LR contributed by each dataset toward the predicted confidence score of a gene pair (Figure 1) was set to the LR observed for the corresponding dataset bin where the gene pair was found (Figure 2). Then the maximal LR among all the datasets of the same data type was chosen to represent the LR contributed by the particular data type toward the prediction score. Finally the ratios determined by different data types were multiplied to represent the combined LR [25,27]. Base 2 logarithmic form of LRs (LLRs) was used to calculate the final score.
Cross validation of the integration results
To evaluate the overall performance of the prediction, we did a ten-fold cross-validation. First we randomly divided both the GSP and GSN datasets into ten separate equal sets. Then we used nine of the ten sets as the training set to calculate the LRs and the remaining set as the test set to identify the positives and negatives. We ran this process ten times so that each of the ten sets was a test set and the remaining nine constituted the training set. Finally we summed up the number of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN) to get the sensitivity (TP/(TP+FN)) and TP/FP ratios under different LLR cutoffs (from 1 to 16). The TP/FP ratios are apparently correlated with LLR cutoffs (Figure 3A). They decrease with the increase in prediction sensitivity, indicating a tradeoff between the accuracy and sensitivity of predictions (Figure 3B). We set a LLR cutoff of 6.0 as the minimal requirement for a predicted PPI to be entered into IntNetDB, and 7.0 as the default prediction level because the TP/FP ratio is greater than 1 at this resolution. That is, at a LLR>7.0 we can get 56% of true positive protein-protein interactions within the 180,010 predicted protein-protein interactions among 9,901 proteins (Figure 3A).
Figure 3. TP/FP ratios (true positive versus false positive) at different LLR cutoffs or sensitivity by 10-fold cross-validation. The TP/FP ratios and the sensitivity (TP/(TP+FN)) are calculated for different LLR (log2LR) cutoffs. Each dot on the curves represents an average of ten cross-validations at a particular LLR cutoff. A. TP/FP ratios versus LLR cutoffs. A resolution of 44% false positives (TP/FP>1) corresponds to a LLR cutoff of 7.0 and 180,010 predicted interactions. Predicted interactions of higher confidence (larger TP/FP ratios) can be obtained by selecting LLR cutoffs higher than 7.0. B. TP/FP versus sensitivity. The TP/FP ratios indicate the accuracy at a certain resolution, while the sensitivity defines the ability of a test to detect true positives. Hence a tradeoff exists between accuracy and sensitivity of a prediction.
Improvements over the previous integration analysis for human PPI predictions
To obtain PPI predictions of higher quality and coverage, we used the latest HPRD dataset as the GSP dataset. HPRD now contains more than 10,000 newly annotated PPIs since the previous analysis of Rhodes et al. We integrated three more types of data (phenotype, genetic interactions and gene context) than used previously. For the interactome data type, we added three recently published interactome datasets [7,8,51], among which two are yeast two-hybrid screens using human proteins. For each new data type we have demonstrated the predictive power. We integrated two genetic interaction datasets, three gene context datasets [21,22] and three recently published phenotypic datasets of model organisms [52-54] (Table 1). The three more data types and the 14 extra datasets (more than double those in the previous analysis) integrated here make our analysis more information-rich, and also enabled us to predict 141,631 additional potential PPIs (4.7-fold increase in coverage) within a similar range of TP/FP ratio [27]. To facilitate use for researchers who are not familiar with network biology or bioinformatics, we have also implemented an easy-to-use and flexible data download, network visualization and analysis options online.
Table 1. Improvements over the previous integration analysis for human PPI predictions
Protein-protein interaction search
IntNetDB provides a web-accessible query interface for users to search for potential PPIs among a list of query genes (Figure 4A). Query genes can be entered batch-wise using a variety of gene identifiers (NCBI Entrez GeneID, GeneSymbol, or GeneBank accession number) for different organisms (C. elegans, D. melanogaster, M. musculus, and H. sapiens). As yeast PPIs have been predicted comprehensively with various methods, we choose not to redo yeast PPI predictions with interolog information from other organisms, but rather refer readers to DIP, BIND and GRID PPI databases for this purpose [60-62], which are more comprehensive for yeast proteins. The purpose of this study was to generate a completely up-to-date PPI prediction database for higher organisms, information which is still lacking and greatly needed. For organisms other than human, no high quality PPI datasets are available to be used as a GSP (gold standard positive). Mapping by predicted interologs is the best that can be reached currently. The lack of a GSP also means it is not possible to estimate the accuracy of the predicted interologs. Therefore, instead of presenting model organism PPI as rigorous data mining conclusions, we provide information on worm, fly and mouse solely for reference and convenience of researchers. Other resources, such as Homologene [63] COG [64] and Inparanoid [65] may provide more precise ortholog information than what we provide here through reciprocal best hit method.
Figure 4. Overview of IntNetDB. A. The IntNetDB web interface. The default example is the network among human p53 (encoded by the TP53 gene) and its potential interaction partners. B. IntNetDB search results. For these query genes, the network graph view shows all predicted PPIs and the data types supporting each PPI, where '-' and '+' signs stand for 'absent' and 'present', respectively. A present call corresponds to LLR>0 for the data type shown. C. Visualization of the predicted PPI network. A hyperlink on the node and edge, when clicked, leads to more detailed information of the node or interaction (insets). D. Graphical representation of highly connected subgraphs. The subgraphs are extracted from the entire network by the MCODE algorithm. The two enlarged subgraphs correspond to the troponin-related complex and the proteasome complex. The color of an edge denotes the evidence type supporting the predicted PPI. 'Multiple evidences' are said to support a predicted interaction when more than one data type has LLR>1 (LR>2) for that edge. The color of a node is assigned according to the GO term of the gene.
IntNetDB also provides information on LLR cutoffs. Users can obtain more reliable PPIs by selecting larger cutoff values, than the default value of 7.0. If users need as many predicted PPIs as possible for analysis, then the predicted PPIs with LLR>6.0 are also accessible from the website. Lower LLR includes more protein-protein interactions and proteins but of lower confidence. Confidence levels (percentage of expected true positive) of the predicted PPIs at different LLR cutoffs are also provided (Figure 3A inset). From the seven integrated heterogeneous data types currently available, users can select the exact data types that they want to access. A list of the PPIs under the user-defined LLR cutoff with data type information are generated after a gene list is submitted. The default gene list uses p53 and its interacting partners in human as an example. A tab-delimited text file of PPIs can be downloaded directly from the query result page (Figure 4B).
Network visualization
Network visualization provides a more informative way than a simple textual list for exploration of a network. IntNetDB provides an online visualization tool for protein-protein interaction networks called 'intView'. A PPI network is represented as an undirected graph by intView, where nodes represent proteins and edges correspond to potential PPIs. Figure 4C shows the network layout of TP53 and its partners. Users can click the nodes or edges to see more information about them (Figure 4C inset). When an edge is clicked, a pop-up panel will display which data type(s) support the functional interaction between the two nodes. Different data types are denoted by different colors, and the width of the edge representing a data type is proportional to the LR value derived from that data type (Figure 4C inset). Online visualization is displayed with a scalable vector graphics (SVG) file. Postscript file and Cytoscape-compatible GML file can also be generated for download.
Extracting highly connected modules
Densely interconnected neighborhoods or clusters in a network frequently correspond to functional modules [46,66]. IntNetDB provides a tool to extract and reveal such neighborhoods or clusters in the network of query genes. For this IntNetDB uses the MCODE algorithm [46], which was created by Bader et al. for predicting yeast protein complexes and implemented by Lee et al for automatically searching protein complexes in any organism [67]. The resulting modules can be visualized by clicking on the 'Network cluster' button on the web page. All the extracted clusters in IntNetDB are visualized in Cytoscape [68] (Figure 4D). For cross examination and fine control, the users are referred to a probabilistic model-based cluster-finding algorithm implemented in NetworkBlast [69].
An advantage of extracting such densely connected neighborhoods is that new functions can be assigned to a gene, or potential functions can be assigned to a gene of unknown function based on the functions of other genes in the same cluster [19]. As an example take the Troponin complex, a key protein complex regulating sarcomeric muscle contraction that is composed of three subunits, Tn-I, Tn-C and Tn-T. The Tn-I subunit inhibits actomyosin ATPase, while the Tn-T binds Tn-C and has high affinity for tropomyosin. With the release of intracellular calcium, Tn-C subunit binds calcium and the conformation of the troponin complex changes accordingly to overcome inhibition of actomyosin ATPase activity [70]. In the predicted human troponin complex, TNNI3 (coding for Tn-I subunit) directly interacts with CASQ1, a protein that binds and putatively stores calcium ions in the sarcoplasmic reticulum. This finding suggests that the Troponin complex may also regulate muscle contractions through direct association with intracellular calcium stores. The network modules also show the Tronponin complex tightly connected with structural proteins (MYH6, MYL3, MYL7, MYBPC3), suggesting that they may also function in the sarcomeric muscle contraction (Figure 4D inset).
Another example of an extracted functional module is the proteasome complex involved in ATP/ubiquitin-dependent peptide cleavage (Figure 4D inset). The proteasome is a multicatalytic proteinase complex comprised of many subunits. In the extracted network module a hypothetical protein, FLJ11848, is predicted to tightly interact with many subunits of the proteasome. Interestingly, FLJ11848 contains six WD40 domains, which are frequently found in adaptor or regulatory proteins. This observation suggests that FLJ11848 might have an important role in regulating the activity of the proteasome. This prediction has been validated by recent work demonstrating that FLJ11848 functions as a negative regulator of the proteasome by controlling the assembly/disassembly of the proteasome [71].
Data and GUI updating scheme
To keep IntNetDB up to date, we will keep tracking newly published large-scale genomic and proteomic datasets, evaluate the performance of them for PPI prediction, and use them to update the IntNetDB. The addition of new datasets should increase the comprehensiveness of the human interactome. To allow for future extension, and to avoid the burden of updating the user interface each time new data are added, GUI items are dynamically generated from database entries to reflect the current data types available in IntNetDB
We have integrated and evaluated potential PPIs from different up-to-date genomic and proteomic features. We have provided a user-friendly query and visualization platform which can be easily extended in the future when more data become available. The product of this effort, IntNetDB, will facilitate network and functional analysis.
Gold standard
The Human Protein Reference Database (HPRD) [47] is a manually annotated protein-protein interaction database with 19,438 distinct interactions among 5,983 proteins. As it is derived from the literature of high quality experimental results, we used it as the gold standard positive (GSP) dataset. We used the gold standard negative (GSN) dataset previously generated by Rhodes et al [27], which includes all possible pair-wise combinations between two sets of proteins that are assigned a subcellular localization of the plasma membrane (1,397 proteins) and the nucleus (2,224 proteins), respectively, by the Gene Ontology (GO) Consortium. The whole GSN set includes a total of 3,106,928 protein pairs. Ideally the GSP and GSN should have no overlapping interactions. Of the 19,438 protein pairs in the positive gold-standard, 4,863 protein pairs are both of known subcellular localization. Of these 4,863 protein pairs, there are 330 overlapping interactions (representing a fraction of 7% = 330/4,863). This is very small compared to the randomly expected size of the intersection (representing a fraction of 38% = 1856/4,863), which was computed by assigning the protein with the shuffled subcellular localization in the GSN set. Although the gold-standard sets are imperfect, they still can provide good approximations for PPI prediction.
HPRD dataset [47] was downloaded on November 13, 2005. GO annotations were downloaded from NCBI on March 10, 2005. The three recently published interactome datasets [7,8,51], the two genetic interaction datasets [21,22], the three gene context datasets [11] and the three recently published phenotypic datasets of the model organisms [52-54] were downloaded from the journal or authors' websites.
Naïve Bayes model
We used the Naïve Bayes method described in Jansen et al. and Rhodes et al. [25,27]. We defined as positive when two proteins interact and as negative when they do not. Considering the total number of positive pairs within all the possible protein pairs, the prior odds of finding a positive pair is:
where P(positive) is the possibility of getting an interacting pair of proteins in all the possible interactions while P(negative) stands for the possibility of getting a pair of non-interacting proteins. In contrast, the posterior odds are the odds of getting a positive when we consider the given evidence:
where the evidence is a data type used to infer PPI between the proteins. The terms 'prior' and 'posterior' refer to the condition before and after considering the information provided by the N evidences. Then the likelihood ratio (L) is defined as:
which relates prior and posterior odds according to the Bayes rule:
Oposterior = L(evidence1...evidenceN)*Oprior.
When N evidences are derived independently, the Bayes rule can be simplified to Naïve Bayes rule and L can be simplified as:
The Likelihood ratio (L) of evidence can be calculated from the positive and negative hits by binning all the evidences into discrete intervals. Then the integrated L can be multiplied from all the independent evidences.
Human orthologs in mouse, fly, worm and yeast were identified as the best reciprocal BlastP hits with e-value cutoff of 10-6 based on RefSeq protein sequences downloaded on December 9, 2004.
Availability and requirements
The web interface of IntNetDB is freely accessible at http://hanlab.genetics.ac.cn/IntNetDB.htm webcite. The graphical layouts are based on SVG, which requires Mozilla version 1.8 and up or installation of SVGviewer for Internet Explorer.
Authors' contributions
J.-D.J.H. conceived and directed the project, designed the analyses, and analyzed data. K.X. and D.D. implemented the analyses, constructed the database and developed the website. J.-D.J.H., K.X. and D.D. wrote the paper. All authors read and approved the final manuscript.
We thank Dr. Michael Cusick for carefully reading and editing our manuscript. This work was supported by grants from the China National Science Foundation (Grant # 30588001, 90508006) and the Hundred Talents Plan of the Chinese Academy of Sciences to J.-D.J.H.
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