Hypoxia-inducible factor-2a is associated with ABCG2 expression, histology-grade and Ki67 expression in breast invasive ductal carcinoma

Lei Xiang
Zhi-Heng Liu
Qin Huan
Peng Su
Guang-Jun Du
Yan Wang
Peng Gao
Geng-Yin Zhou
Hypoxia-inducible factor-2a is associated with ABCG2 expression, histology-grade and Ki67 expression in breast invasive ductal carcinoma
R E S E A R C H Open Access
Background: Breast cancer is the most common cancer and the leading cause of cancer mortality in women worldwide. Hypoxia is an important factor involved in the progression of solid tumors and has been associated with various indicators of tumor metabolism, angiogenesis and metastasis. But little is known about the contribution of Hypoxia-Inducible Factor-2a (HIF-2a) to the drug resistance and the clinicopathological characteristics in breast cancer. Methods: Immunohistochemistry was employed on the tissue microarray paraffin sections of surgically removed samples from 196 invasive breast cancer patients with clinicopathological data. The correlations between the expression of HIF-2a and ABCG2 as well as other patients' clinicopathological data were investigated.Results:The results showed that HIF-2a was expressed in different intensities and distributions in the tumor cells of the breast invasive ductal carcinoma. A positive staining for HIF-2a was defined as a brown staining observed mainly in the nucleus. A statistically significant correlation was demonstrated between HIF-2a expression and ABCG2 expression (p = 0.001), histology-grade (p = 0.029), and Ki67 (p = 0. 043) respectively.Conclusion: HIF-2a was correlated with ABCG2 expression, histology-grade and Ki67 expression in breast invasive ductal carcinoma. HIF-2a could regulate ABCG2 in breast cancer cells, and could be a novel potential bio-marker to predict chemotherapy effectiveness. The hypoxia/HIF-2a/ABCG2 pathway could be a new mechanism of breast cancer multidrug-resistance. Virtual slides: http://www.diagnosticpathology.diagnomx.eu/vs/2965948166714795

Background

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer mortality in women worldwide. Clinically, breast cancer is a remarkably heterogeneous disease in terms of gene expression, morphology, clinical course, and response to treatment. Traditionally, pathologic determinations of tumor size, lymph node status, endocrine receptor status, and human epidermal growth factor receptor 2 (HER2) status have driven prognostic predictions and, ultimately, adjuvant therapy recommendations for patients with early stage breast cancer. In recent years, many potential new prognostic markers of a biochemical nature have been described for breast cancer. These include steroidhormone receptors, growth factor receptors, activated proto-oncogenes, and proteolytic enzymes et al.

Hypoxia is an important factor involved in the progression of solid tumors and has been associated with various indicators of tumor metabolism, angiogenesis and metastasis [1]. The presence of widespread hypoxia within tumors has been associated with reduced survival after radiotherapy or chemotherapy. Hypoxia has also been linked to poor outcome in a number of tumors regardless of the treatment modalities used. Karolina Helczynska et al. [2] found a significant association between Hypoxia-Inducible Factor-2a (HIF-2a) protein and adverse prognosis of breast cancer, and no such association was found for Hypoxia-Inducible Factor-1a. In other words, HIF-2a is a possible potential independent prognostic bio-marker of breast cancer.

ABCG2 (ATP-binding cassette sub-family G member 2), or breast cancer resistance protein (BCRP), is an vital trans-membrane transporter which plays an important role in the multidrug resistance (MDR) of breast cancer.

Recently, our research team found that ABCG2 is associated with HER-2 expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma using immunohistochemistry (IHC) stain on the tissue microarray paraffin sections of surgically removed samples from 196 breast cancer patients with clinicopathological data, which means ABCG2 may be a novel potential prognostic bio-marker which can predict biological behavior, clinical progression, prognosis and chemotherapy effectiveness [3].

Two functional elements in the ABCG2 promoter, the estrogen [4] and hypoxia [5] response elements (HRE), and a peroxisome proliferator-activated receptor g (PPARg) response element upstream of the ABCG2 gene [6] have been shown to control ABCG2 expression. So, it is interesting to explore the expression of these two potential prognostic bio-markers, HIF-2a and ABCG2, and their possible correlations in primary breast cancer.

In the present study, the expression of HIF-2a and ABCG2 was detected by immunohistochemistry using tissue microarray according to immunohistochemical phenotypes and the correlationships between HIF-2a and ABCG2 expression/the clinicopathological data were discussed. We demonstrated a possibility of predictive role of HIF-2a in chemotherapy of breast cancer.

Materials and methods

Patients and tissue samples

We retrieved tissue samples from patients with breast invasive ductal carcinoma in the Department of Pathology of Qilu Hospital of Shandong University during July 2007 through December 2008. Formalin-fixed and paraffin-embedded tissue specimens from 196 patients with primary breast cancer were included. All archival hematoxylin and eosin (H&E)-stained slides for each patient were reviewed by two pathologists. For the usage of the clinical materials for research purposes, prior patient content and approval from the Institutional Research Ethics Committee were obtained. All the diagnoses were made following the Pathology and Genetics of Tumors of Breast of the World Health Organization Classification of Tumors [7]. Clinicopathologic classification and staging were determined according to the American Joint Committee on Cancer criteria [8].

The histological grade was assessed using the Nottingham grading system [9], and nuclear grade was evaluated according to the modified Black's nuclear grade [10]. Histological parameters such as histological subtype, nuclear grade and histological grade were evaluated according to H&E-stained slides. Clinical parameters included patients' age, tumor size, lymph node status, clinical stage and biological markers (ER, PR, HER2 and ki67 et al.).

Tissue microarray

For each H&E-stained slide, two representative areas were selected and the corresponding spots were marked on the surface of the paraffin block. Using a tissue microarray punching instrument, the selected areas were punched out and were placed into the recipient block side by side. Each tissue core was 2 mm in diameter and was assigned with a unique tissue microarray location number that was linked to a database containing other clinicopathologic data.

Immunohistochemistry (IHC)

The streptavidin-peroxidase-biotin (SP) immunohistochemical method was utilized to study the expression of HIF-2a and ABCG2 in 196 paraffin-embedded breast tissues.

In brief, paraffin-embedded specimens were cut into 4 μm sections and baked at 60°C for 60 min. The sections were deparaffinized with xylenes and rehydrated. Then sections were submerged into EDTA antigenic retrieval buffer in a pressure cooker for 10 min and then cooled at room temperature for 20 min. The sections were treated with 3% hydrogen peroxide in methanol to quench the endogenous peroxidase activity, followed by incubation with normal serum to block nonspecific binding. Mouse monoclonal HIF-2 alpha antibody [ep190b] (1:2000; Abcam company, ab8365, USA) and Mouse monoclonal ABCG2 antibody [BXP-21] (1:50; Abcam Company, ab3380, USA) were incubated with the sections overnight at 4°C; the second antibody was from an SP reagent kit (Zhongshan Biotechnology Company, Beijing, China). After washing, the tissue sections were treated with biotinylated anti-mouse secondary antibody, followed by further incubation with streptavidin-horseradish peroxidase complex for 20 mins. Stained with diaminobenzidine (DAB), the sections were counterstained with hematoxylin. For negative controls, the anti-HIF-2a and anti-ABCG2 antibodies were replaced with PBS.

Evaluation of immunohistochemical staining

The stained slides were reviewed and scored independently by two observers blinded to the patients' information, and the scores were determined by combining the proportion of positively stained tumor cells and the intensity of staining. Tumor cell proportion was scored as follows [11]: 0 (≤ 10% positive tumor cells); 1 (≤ 30% positive tumor cells); 2 (31-50% positive tumor cells); 3 (51-80% positive tumor cells) and 4 (> 80% positive tumor cells). Staining intensity was graded according to the following criteria: 0 (-, no staining); 1 (+, weak staining = light yellow); 2 (++, moderate staining = yellow brown) and 3 (+++, strong staining = brown). Staining index (SI) was calculated as the product of the staining intensity score and the proportion of positive tumor cells. Using this method of assessment, we evaluated HIF-2a and ABCG2 expression in invasive breast cancer cells by determining the SI, with scores of 0,1, 2, 3, 4, 6, 9 or 12. The optimal cutoff value for high and low expression level was identified: an SI score of ≥ 4 was used to define tumors with high expression of HIF-2a and ABCG2, and an SI score of ≤ 3 was used to indicate low expression of HIF-2a and ABCG2, and the SI score of 0 was used to imply negative expression, respectively.

Statistical analysis

The chi-square test or Fisher's exact test were used to evaluate the correlation between HIF-2a expression and ABCG2 expression, as well as the clinicopathologic characteristics if appropriate. Statistical Analyses were performed using the statistical software package SPSS 13.0 (SPSS, Chicago, IL). Differences were considered statistically significant for p < 0.05.

Results

The specificity of the immunodetection was confirmed by using the monoclonal antibodies HIF-2a antibody [ep190b]. A positive stain for HIF-2a was defined as a brown stain observed mainly in the nucleus, and a negative or weak cytoplasmic reactivity observed. Positive staining of normal adjacent ductal epithelia as well as vascular endothelium and stromal cells of the breast has a low level expression (Figure 1). And it can serve as an internal positive control.

In the cells of the breast invasive ductal carcinoma, HIF-2a expression was present in different intensities and different cell distributions. Following the staging criteria of stain intensity, 12 cases (6.12%) were identified as completely negative (Figure 2), 78 cases (39.80%) were identified as "+" (Figure 3), 69 cases (35.20%) were identified as "++" (Figure 4), and 37 cases (18.88%) were identified as "+++" (Figure 5). According to the above assessment methods and evaluation criterion, combining the proportion of positively stained tumor cells, the negative, low level and high level expression of HIF-2a was observed in 25 cases (12.76%), 114 cases (58.16%) and 57 cases (29.08%), respectively.

The IHC expression and distribution of ABCG2 in invasive breast cancer cells are as same as the findings of our previous study [3].

There was no significant correlation between the expression level of HIF-2a and biological factors such as patients' age (p = 0.053), histology type (p = 0.285), tumour size (p = 0.601), lymph node metastasis (p = 0.808), ER (p = 0.544), PR (p = 0.343), HER-2 expression (p = 0.923) and clinical stage (p = 0.538). In contrast, statistical analyses indicated that HIF-2a expression was positively related with ABCG2 expression and the correlation was statistically significant (p = 0.001); meanwhile, the correlation between HIF-2a expression and histology-grade/Ki67 was significant (p = 0.029/0.043, respectively). The results are summarized in Table 1.

Discussion

HIF-2α, also known as endothelial PAS domain protein 1 (EPAS1) or member of PAS superfamily 2 (MOP2)    [12][13][14][15], was the second HIF family member to be identified and belonged to the basic helix-loop-helix (bHLH)/Per-ARNT-Sim (PAS) domain family of transcription factors [16]. It activates gene expression via formation of a dimeric complex with HIF-1β (also called aryl hydrocarbon receptor nuclear translocator, ARNT) and subsequent binding to hypoxia response elements (HREs) within target genes. Among its transcription    targets are genes involved in proliferation, metabolism, angiogenesis, differentiation, and metastasis [1]. Numerous immunochemical analyses have demonstrated that HIF-2α was over-expressed in a number of primary and metastatic human cancers, and that the level of expression, either as a result of tumor hypoxia or genetic alterations, is correlated with tumor angiogenesis and patient mortality. High HIF-2α expression has been linked to poor patient outcome in several tumor types [17][18][19][20][21].

In the present study, we studied HIF-2α expression in paraffin-embedded tumor samples using the IHC method with the Abcam Mouse monoclonal HIF-2 alpha antibody [ep190b, ab8365], and the positive stain for HIF-2α is located mainly in the nucleus of cells as the product leaflet of the antibody indicates.

In our study, the expression HIF-2α of in most tumor cells (87.24%) of the breast invasive ductal carcinoma is positive, in which about 1/3 cases present relatively high HIF-2α expression in the breast cancer cells. Furthermore, the expression of HIF-2α is correlated with the histology-grade (p = 0.029) and the expression of Ki67 (p = 0.043). Those patients with high expression of HIF-2α were demonstrated to be more frequently showing high histology-grade and high expression of Ki67, which means that HIF-2α have some correlation with worse biological behavior and clinical aggressiveness.

To the best of our knowledge, this is the first report that the expression of HIF-2α is correlated with histology-grade and the expression of Ki67 in the cohort of breast invasive ductual carcinoma. We think it is of great importance for further research. In general, it is consistent with previous literature that high expression of HIF-2α have a worse prognosis than those with low expression. Because there was no case in clinical IV stage in the 196 patients which were used in the study, it is impossible to evaluate the correlation between HIF-2α expression and cases with distant metastasis.

In the study of Helczynska K et al. [2], the expression of HIF-2α is located both in nucleus and cytoplasm of the tumor cells by IHC detection and show a significant correlation to incidence of distant recurrence. We think the different antibodies and clinicopathologic variables used in the two studies may contribute the different results between the study of Helczynska K et al. and ours.

ABCG2 (or BCRP), is the second member of the G subfamily of the ATP-binding cassette (ABC) efflux transporter superfamily that has been the subject of intense study.

In our previous study [3], we found the expression of ABCG2 protein correlated with Her-2 expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma and ABCG2 could be a novel potential bio-marker which can predict biological behavior, clinical progression, prognosis and chemotherapy effectiveness.

In the present study, we found HIF-2α expression was correlated with ABCG2 expression significantly (p = 0.001) in paraffin-embedded tumor samples using the IHC method. It means that those patients with high expression of HIF-2α were demonstrated to be more frequently showing high immunoreactions with ABCG2, which suggests that HIF-2α may have some correlation with worse chemotherapy effectiveness. The finding is consistent with the study of Cindy M. Martin et al. [22], in whose report HIF-2α was found being a potent transcriptional regulator of the Abcg2 gene, and HIF-2α bond an evolutionary conserved HIF-2α response element (HRE) in the murine Abcg2 promoter. A hypothesis had been raised by Cindy M. Martin et al. that Abcg2 is a direct downstream target of HIF-2α in the side population (SP) progenitor cell population of the adult heart. But in the area of cancer research, there is no such kind of report. To the best of our knowledge, this is the first report that the expression of HIF-2α is correlated with ABCG2 expression in the cohort of breast invasive ductual carcinoma. Our findings could be another evidence for the above hypothesis. And in the area of the mechanism research of breast cancer MDR, our findings pointed out another possible genesis mechanism that hypoxia could induce the expression of ABCG2 by HIF-2α, which promoted the multidrug resistance of breast cancer cells.

Because of the evaluation of IHC staining and samples selecting of tissue microarray, some errors of our study could be possible. Whether HIF-2a is correlated with the prognosis of breast cancer and whether the above possible regulation pathways is correct in cancer cells need to be explored by further study.

Conclusion

Our study demonstrates the expression of HIF-2α in normal ductal epithelia and in the tumor cells of invasive ductal carcinoma of breast. Its expression is correlated significantly with that of histology-grade and Ki67 in invasive ductal breast cancer. The study also found HIF-2α expression was correlated with ABCG2 expression significantly, which suggests HIF-2α be a potent transcriptional regulator of the Abcg2 gene and Abcg2 be a direct downstream target of HIF-2α in the cells of invasive ductal breast cancer. These findings suggest that HIF-2α is not only one of the hypoxia inducible factors, but also a novel potential bio-marker which could predict worse chemotherapy effectiveness in invasive ductal carcinoma of breast; and indicate that hypoxia promoted the multidrug resistance of breast cancer cells by hypoxia/HIF-2α/ABCG2/MDR pathway.

Further investigation on the molecular mechanism of possible regulation relationships between them should be done.

Figure 1
1Normal breast tissue, HIF-2a positive (IHC, SP × 200). The HIF-2a staining is weak, and mainly localized mainly in the nucleus of the glandular epithelium cells and vascular endothelium of the breast.

Figure 2
2Breast cancer tissue, HIF-2a negative (IHC, SP × 200). The HIF-2a staining is almost negative.

Figure 3
3Breast cancer tissue, HIF-2a positive + (IHC, SP × 200). The HIF-2a staining is weak. Left top shows the normal duct of the breast.

Figure 4
4Breast cancer tissue, HIF-2a positive ++ (IHC, SP × 200). The HIF-2a staining is moderate.

Figure 5
5Breast cancer tissue, HIF-2a positive +++ (IHC, SP × 200). The HIF-2a staining is strong.

Table 1
1The correlation between the expression of HIF-2 alpha and BCRP, the clinicopathological parameterPatients n (%)
HIF-2 alpha
expression

P value

negative low high

ABCG2 expression
0.001*

negative
26
8
17
1

low
97
12
58
27
high
73
5
39
29

Age
0.053

≤ 50 years
80
10
54
16

> 50 years
116
15
60
41

Histology-type
0.285

IDC
155
22
86
47

IDC with others 41
3
28
10

Histology-grade
0.029*

G1/G2
143
16
78
49

G3
53
9
36
8

Tumour size
0.601

≤2.0 cm
97
13
53
31

> 2.0 cm
99
12
61
26

LNM
0.808

-
103
13
58
32

+
9 3
1 2
5 6
2 5

ER
0.544

-/+
96
10
59
27

++/+++
100
15
55
30

PR
0.343

-/+
128
14
79
35

++/+++
68
11
35
22

HER2
0.923

-/+
138
18
81
39

++/+++
58
7
33
18

Ki67
0.043*

Positive ≤50%
154
20
83
51

Positive > 50% 42
5
31
6

Clinical stage
0.538

Stage I
60
7
33
20

Stage IIa and IIb 86
13
47
26

StageIIIa and IIIb 50
5
34
11

P values were evaluated by chi-square test or the Fisher's exact test. *P < 0.05

Xiang et al. Diagnostic Pathology 2012, 7:32 http://www.diagnosticpathology.org/content/7/1/32 Page 2 of 6
AcknowledgementsThis study was supported by the Shandong Provincial Nature Science Foundation, China (Grant No. ZR2010HQ056).Authors' contributions LX and YW did the immunohistochemical analysis. PS and PG reviewed all the pathological slides. ZL and QH made the tissue microarray. GD analyzed the data. GZ designed the study. LX drafted the manuscript. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
Hypoxia-inducible factors: central regulators of the tumor phenotype. J D Gordan, M C Simon, Curr Opin Genet Dev. 17Gordan JD, Simon MC: Hypoxia-inducible factors: central regulators of the tumor phenotype. Curr Opin Genet Dev 2007, 17:71-77.

Hypoxia-inducible factor 2a correlates to distant recurrence and poor outcome in invasive breast cancer. K Helczynska, A M Larsson, L Holmquist-Mengelbier, E Bridges, E Fredlund, S Borgquist, G Landberg, S Pahlman, K Jirstrom, Cancer Res. 689212Helczynska K, Larsson AM, Holmquist-Mengelbier L, Bridges E, Fredlund E, Borgquist S, Landberg G, Pahlman S, Jirstrom K: Hypoxia-inducible factor 2a correlates to distant recurrence and poor outcome in invasive breast cancer. Cancer Res 2008, 68:9212.

Zhou Genyin: ABCG2 is associated with HER-2 Expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma. Xiang Lei, Su Peng, Xia Shujun, Liu Zhiyan, Wang Yan, Gao Peng, 10.1186/1746-1596-6-90Diagnostic Pathology. 690Xiang Lei, Su Peng, Xia Shujun, Liu Zhiyan, Wang Yan, Gao Peng, Zhou Genyin: ABCG2 is associated with HER-2 Expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma. Diagnostic Pathology 2011, 6:90, doi:10.1186/1746-1596-6-90.

Identification of a novel estrogen response element in the breast cancer resistance protein (ABCG2) gene. P L Ee, S Kamalakaran, D Tonetti, X He, D D Ross, W T Beck, Cancer Res. 64Ee PL, Kamalakaran S, Tonetti D, He X, Ross DD, Beck WT: Identification of a novel estrogen response element in the breast cancer resistance protein (ABCG2) gene. Cancer Res 2004, 64:1247-1251.

The stem cell marker Bcrp/ABCG2 enhances hypoxic cell survival through interactions with heme. P Krishnamurthy, D D Ross, T Nakanishi, K Bailey-Dell, S Zhou, K E Mercer, B Sarkadi, B P Sorrentino, J D Schuetz, J Biol Chem. 279Krishnamurthy P, Ross DD, Nakanishi T, Bailey-Dell K, Zhou S, Mercer KE, Sarkadi B, Sorrentino BP, Schuetz JD: The stem cell marker Bcrp/ABCG2 enhances hypoxic cell survival through interactions with heme. J Biol Chem 2004, 279:24218-24225.

Peroxisome proliferator-activated receptor gamma-regulated ABCG2 expression confers cytoprotection to human dendritic cells. I Szatmari, G Vamosi, P Brazda, B L Balint, S Benko, L Szeles, V Jeney, C Ozvegy-Laczka, A Szanto, E Barta, J Balla, B Sarkadi, L Nagy, J Biol Chem. 281Szatmari I, Vamosi G, Brazda P, Balint BL, Benko S, Szeles L, Jeney V, Ozvegy- Laczka C, Szanto A, Barta E, Balla J, Sarkadi B, Nagy L: Peroxisome proliferator-activated receptor gamma-regulated ABCG2 expression confers cytoprotection to human dendritic cells. J Biol Chem 2006, 281:23812-23823.

Intraductal proliferative lesions. Pathology and genetics of tumors of the breast and female genetic organs Lyon: International Agency for Research on. F A Tavassoli, P Devilee, Cancer. IARC) PressTavassoli FA, Devilee P: Intraductal proliferative lesions. Pathology and genetics of tumors of the breast and female genetic organs Lyon: International Agency for Research on Cancer (IARC) Press; 2003.

Breast Cancer in AJCC Cancer Staging Handbook. TNM Classification of Malignant Tumors. F L Greene, D L Page, I D Fleming, Springer Verlag6New YorkGreene FL, Page DL, Fleming ID: Breast Cancer in AJCC Cancer Staging Handbook. TNM Classification of Malignant Tumors. New York: Springer Verlag; 2002, 6.

Experience from a large study with long-term followup. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer. C W Elston, I O Ellis, Histopathology. 19Elston CW, Ellis IO: Experience from a large study with long-term follow- up. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer. Histopathology 1991, 19:403-410.

Further observations on prognostic factors in cancer of the female breast. S J Cutler, M M Black, T Mork, S Harvei, C Freeman, Cancer. 24Cutler SJ, Black MM, Mork T, Harvei S, Freeman C: Further observations on prognostic factors in cancer of the female breast. Cancer 1969, 24:653-667.

Research Immunohistochemical analysis of Metadherin in proliferative and cancerous breast tissue. P Su, Q Zhang, Q Yang, 10.1186/1746-1596-5-38Diagn Pathol. 538Su P, Zhang Q, Yang Q: Research Immunohistochemical analysis of Metadherin in proliferative and cancerous breast tissue. Diagn Pathol 2010, 5:38, doi:10.1186/1746-1596-5-38.

Endothelial PAS domain protein 1 (EPAS1), a transcription factor selectively expressed in endothelial cells. H Tian, S L Mcknight, D W Russell, Genes Dev. 11Tian H, Mcknight SL, Russell DW: Endothelial PAS domain protein 1 (EPAS1), a transcription factor selectively expressed in endothelial cells. Genes Dev 1997, 11:72-82.

A novel bHLH-PAS factor with close sequence similarity to hypoxia-inducible factor 1alpha regulates the VEGF expression and is potentially involved in lung and vascular development. M Ema, S Taya, N Yokotani, K Sogawa, Y Matsuda, Y Fujii-Kuriyama, Proc Natl Acad Sci. 94Ema M, Taya S, Yokotani N, Sogawa K, Matsuda Y, Fujii-Kuriyama Y: A novel bHLH-PAS factor with close sequence similarity to hypoxia-inducible factor 1alpha regulates the VEGF expression and is potentially involved in lung and vascular development. Proc Natl Acad Sci USA 1997, 94:4273-4278.

HRF, a putative basic helix-loop-helix-PAS-domain transcription factor is closely related to hypoxia-inducible factor-1 alpha and developmentally expressed in blood vessels. I Flamme, T Frohlich, M Von Reutern, A Kappel, A Damert, W Risau, Mech Dev. 63Flamme I, Frohlich T, von Reutern M, Kappel A, Damert A, Risau W: HRF, a putative basic helix-loop-helix-PAS-domain transcription factor is closely related to hypoxia-inducible factor-1 alpha and developmentally expressed in blood vessels. Mech Dev 1997, 63:51-60.

Characterization of a subset of the basic-helix-loop-helix-PAS superfamily that interacts with components of the dioxin signaling pathway. J B Hogenesch, W K Chan, V H Jackiw, R C Brown, Y Z Gu, M Pray-Grant, J Biol Chem. 272Hogenesch JB, Chan WK, Jackiw VH, Brown RC, Gu YZ, Pray-Grant M, et al: Characterization of a subset of the basic-helix-loop-helix-PAS superfamily that interacts with components of the dioxin signaling pathway. J Biol Chem 1997, 272:8581-8593.

Hypoxia-inducible factors Per/ARNT/Sim domains: structure and function. T H Scheuermann, L Zhang, K H Gardner, R K Bruick, Methods Enzymol. 435Scheuermann TH, Zhang L, Gardner KH, Bruick RK: Hypoxia-inducible factors Per/ARNT/Sim domains: structure and function. Methods Enzymol 2007, 435:3-24.

Contrasting properties of hypoxia-inducible factor 1 (HIF-1) and HIF-2 in von Hippel-Lindau-associated renal cell carcinoma. R R Raval, M G Tran, H M Sowter, S J Mandriota, J L Li, C W Pugh, P H Maxwell, A L Harris, P J Ratcliffe, Mol Cell Biol. 25Raval RR, Tran MG, Sowter HM, Mandriota SJ, Li JL, Pugh CW, Maxwell PH, Harris AL, Ratcliffe PJ: Contrasting properties of hypoxia-inducible factor 1 (HIF-1) and HIF-2 in von Hippel-Lindau-associated renal cell carcinoma. Mol Cell Biol 2005, 25:5678-5686.

Inhibition of HIF is necessary for tumor suppression by the von Hippel-Lindau protein. K Kondo, E Nakamura, M Lechpammer, W G KaelinJr, Cancer Cell. 1Kondo K, Nakamura E, Lechpammer M, Kaelin WG Jr: Inhibition of HIF is necessary for tumor suppression by the von Hippel-Lindau protein. Cancer Cell 2002, 1:237-246.

Inhibition of HIF2alpha is sufficient to suppress pVHL-defective tumor growth. K Kondo, M Lechpammer, W G KaelinJr, Plos Biol. 1Kondo K, Lechpammer M, Kaelin WG Jr: Inhibition of HIF2alpha is sufficient to suppress pVHL-defective tumor growth. Plos Biol 2003, 1:439-444.

Relation ofhypoxia inducible factor 1 alpha and 2 alpha in operable non-small cell lung cancer to angiogenic/molecular profile of tumours and survival. A Giatromanolaki, E Sivridis, H Turley, K Talks, F Pezzella, K C Gatter, A L Harris, Br J Cancer. 85Giatromanolaki A, Sivridis E, Turley H, Talks K, Pezzella F, Gatter KC, Harris AL: Relation ofhypoxia inducible factor 1 alpha and 2 alpha in operable non-small cell lung cancer to angiogenic/molecular profile of tumours and survival. Br J Cancer 2001, 85:881-890.

Recruitment of HIF-1alpha and HIF-2alpha to common target genes is differentially regulated in neuroblastoma: HIF-2alpha promotes an aggressive phenotype. L Holmquist-Mengelbier, T Löfstedt, R Noguera, S Navarro, H Nilsson, A Pietras, J Vallon-Christersson, A Borg, K Gradin, L Poellinger, S Påhlman, Cancer Cell. 10Holmquist-Mengelbier L, Löfstedt T, Noguera R, Navarro S, Nilsson H, Pietras A, Vallon-Christersson J, Borg A, Gradin K, Poellinger L, Påhlman S: Recruitment of HIF-1alpha and HIF-2alpha to common target genes is differentially regulated in neuroblastoma: HIF-2alpha promotes an aggressive phenotype. Cancer Cell 2007, 10:413-423.

C M Martin, A Ferdous, T Gallardo, C Humphries, H Sadek, A Caprioli, J A Garcia, L I Szweda, M G Garry, D J Garry, Hypoxia-Inducible Factor-2α. Martin CM, Ferdous A, Gallardo T, Humphries C, Sadek H, Caprioli A, Garcia JA, Szweda LI, Garry MG, Garry DJ: Hypoxia-Inducible Factor-2α

Transactivates Abcg2 and Promotes Cytoprotection in Cardiac Side Population Cells. 10.1186/1746-1596-7-32Circ Res. 1029Transactivates Abcg2 and Promotes Cytoprotection in Cardiac Side Population Cells. Circ Res 2008, 102(9):1075-1081. doi:10.1186/1746-1596-7-32

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Kevin Bealer firstname.lastname@example.com
BMC Bioinformatics
National Center for Biotechnology Information
National Library of Medicine
National Institutes of Health
Building 38A8600, 20894Rockville Pike, BethesdaMDUSA

Thomas L Madden firstname.lastname@example.com
BMC Bioinformatics
National Center for Biotechnology Information
National Library of Medicine
National Institutes of Health
Building 38A8600, 20894Rockville Pike, BethesdaMDUSA

BLAST+: architecture and applications
10.1186/1471-2105-10-421BioMed Central Page 1 of 9 (page number not for citation purposes) Software * Corresponding author
Background: Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications.Results:We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site.Conclusion:The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.

Background

Basic Local Alignment Search Tool (BLAST) [1,2] is a sequence similarity search program that can be used to quickly search a sequence database for matches to a query sequence. Several variants of BLAST exist to compare all combinations of nucleotide or protein queries against a nucleotide or protein database. In addition to performing alignments, BLAST provides an "expect" value, statistical information about the significance of each alignment.

BLAST is one of the more popular bioinformatics tools. Researchers use command-line applications to perform searches locally, often searching custom databases and performing searches in bulk, possibly distributing the searches on their own computer cluster. The current BLAST command-line applications (i.e., blastall and blastpgp) were available to the public in late 1997. They are part of the NCBI C toolkit [3] and are supported on a number of platforms that currently includes Linux, various flavors of UNIX (including Mac OS X), and Microsoft Windows.

The initial BLAST applications from 1997 lacked many features that are presently taken for granted. Within three years of the initial public release, BLAST was modified to handle databases with more than 2 billion letters, to limit a search by a list of GenInfo Identifiers (GIs), and to simultaneously search multiple databases. PHI-BLAST [4], IMPALA [5], and composition-based statistics [6] were also introduced within this time period, followed by MegaBLAST [7] and the concept of query-concatenation (whereby the database is scanned once for many queries). Chris Joerg of Compaq Computer Corporation suggested performance enhancements in 1999. A group at Apple, Inc. suggested other enhancements in 2002 [8]. These and other features were of great importance to BLAST users, but the continual addition of unforeseen modifications made the BLAST code fragile and difficult to maintain.

Many mammalian genomes contain a large fraction of interspersed repeats, with 38.5% of the mouse genome and 46% of the human genome reported as interspersed repeats [9]. Traditionally, the only supported method available to mask interspersed repeats in stand-alone BLAST has been to execute a separate tool (e.g., Repeat-Masker [10]) on a query, produce a FASTA file with the masked region in lower-case letters, and have BLAST treat the lower-case letters as masked query sequence. This requires separate processing on each query before the BLAST search.

NCBI recently redesigned the BLAST web site [11] to improve usability [12], which helped to identify issues that might also occur in the stand-alone BLAST command-line applications. These changes have, unfortunately, made it more difficult to match parameters used in a stand-alone search with default parameters on the NCBI web site.

The advent of complete genomes resulted in much longer query and subject sequences, leading to new challenges that the current framework cannot handle. At the same time, increases in generally available computer memory made other approaches to similarity searching viable. BLAT [13] uses an index stored in memory. Cameron and collaborators designed a "cache-conscious" implementation of the initial word finding module of BLAST [14]. The concerns listed in this section and the start of a new C++ toolkit at the NCBI [15] motivated us to rewrite the BLAST code and release a completely new set of command-line applications. Here we report on the design of the new BLAST code, the resulting improvements, and a new set of BLAST command-line applications.

In this article, a search type is described by a word or two in all upper-case letters. For example, a BLASTX search translates the nucleotide query in six frames and compares it to a protein database.

Implementation

This section reports first on the overall design of the new software and then discusses several enhancements to BLAST.

Overall design

Two criteria were most important in the design of the new BLAST code: 1.) the code structure should be modular enough to allow easy modification; and 2.) the same BLAST code should be embedded in at least two different host toolkits. This would allow both the new NCBI C++ toolkit and the older NCBI C toolkit to use the same BLAST source code.

At a high level, the BLAST process can be broken down into three modules ( Figure 1). The "setup" module sets up the search. The "scanning" module scans each subject sequence for word matches and extends them. The "traceback" module produces a full gapped alignment with insertions and deletions.

The setup phase reads the query sequence, applies lowcomplexity or other filtering to it, and builds a "lookup" table (i.e., perfect hashing). The lookup table contains only words from the query for nucleotide-nucleotide searches such as BLASTN or MEGABLAST. DISCONTIGU-OUS MEGABLAST allows non-consecutive matches in the initial seed. Protein-protein searches such as BLASTP allow "neighboring" words. The neighboring words are similar to a word in the query, as judged by the scoring matrix and a threshold value.

The scanning phase scans the database and performs extensions. Each subject sequence is scanned for words ("hits") matching those in the lookup table. These hits are used to initiate a gap-free alignment. Gap-free alignments that exceed a threshold score then initiate a gapped alignment, and those gapped alignments that exceed another threshold score are saved as "preliminary" matches for further processing. The scanning phase employs a few optimizations. The gapped alignment returns only the score and extent of the alignment. The number and position of insertions, deletions and matching letters are not stored (no "trace-back), reducing the CPU time and memory demands. Searches against nucleotide subject sequences consider only unambiguous bases (A, C, G, T), with ambiguous bases (e.g., N) replaced at random during preparation of the BLAST database or subject sequence. A four letter alphabet allows packing of four bases into one byte, and the subject sequences are scanned four letters at a time. Finally, less sensitive heuristic parameters are employed for the gapped alignment, and the full extent of a gapped alignment may, in rare cases, not be found.

The final phase of the BLAST search is the trace-back. Insertions and deletions are calculated for the alignments found in the scanning phase. Ambiguous bases are restored for nucleotide subject sequences, and more sensitive heuristic parameters are used for the gapped alignment. Composition-based statistics [6] may also be applied for BLASTP (protein-protein) and TBLASTN (protein compared against translated nucleotide subject sequences). Ideally, one should be able to independently replace the functionality described in each of the small rectangles of Figure 1 (e.g., "build lookup table") with another implementation. Some coordination is required: for example, the lookup table is used when finding word matches, so both "build lookup table" and "find word matches" need to be changed together. Finding word matches is the most computationally intensive part of the BLAST search, so the implementation should be as fast as possible. To address this, the author of the lookup table implementation must provide the scanning routine for finding word hits. Other modules can be changed independently.

The selection of ISO C99 allows use of the new BLAST code in both C and C++ environments. The host toolkit provides a software layer to allow BLAST to communicate with the rest of each toolkit. This design requires a clean separation between the algorithmic part of BLAST and the module that retrieves subject sequences from the database. To allow this, the retrieval of subject sequences for Schematic of a BLAST search Figure 1 Schematic of a BLAST search. The first phase is "setup". The query is read, low-complexity or other filtering might be applied to the query, and a "lookup" table is built. The next phase is "scanning". Each subject sequence is scanned for words ("hits") matching those in the lookup table. These hits are further processed, extended by gap-free and gapped alignments, and scored. Significant "preliminary" matches are saved for further processing. The final phase in the BLAST algorithm, called the "trace-back", finds the locations of insertions and deletions for alignments saved in the scanning phase. processing by the core of the BLAST code is performed through an Abstract Data Type (ADT), which specifies a set of data values and permitted operations. The actual retrieval occurs through an implementation of the ADT in the host toolkit. The implementation can be changed depending upon the need and requires no changes to the BLAST algorithm code itself.

The subject sequence information required by BLAST is quite simple. It consists of the total number of sequences to be searched, the length of any given sequence, as well as methods to retrieve the actual sequence. The total database length is needed for calculation of expect values. A database name and the length of the longest subject sequence are also required to implement some functions in an efficient manner. In order to satisfy the above requirements, an ADT, called the BlastSeqSrc [16], was implemented.

Database masking

Low-complexity regions and interspersed repeats typically match many sequences. These matches are normally not of biological interest, may lead to spurious results, and confound the statistics used by BLAST. BLAST offers two query masking modes to avoid such matches. One is known as "hard-masking" and replaces the masked portion of the query by X's or N's for all phases of the search. On the other hand, "soft-masking" makes the masked portion of the query unavailable for finding the initial word hits, but the masked portion is available for the gapfree and gapped extensions once an initial word hit has been found.

The BLAST databases can also be masked. Masking information is stored as a series of intervals, so that masking can be switched on or off. Information from multiple masking algorithms can be stored in the same BLAST database and accessed separately. Currently, database masking consists of skipping masked portions of the database during the scanning phase, but it is still possible to extend through masked portions of the database; as such, database masking is analogous to soft-masking a query.

Minimizing memory and cache footprint

Modifications that reduce the CPU time and memory footprint of BLAST searches with long query or subject sequences are examined. First, an optimization for the scanning phase of the BLAST search is presented. Then, an improvement for the trace-back phase is described.

BLAST searches with very large queries are routine, but some of the data structures scale with the query length. The following analysis examines the scanning phase (Figure 1) of the BLAST search.

Two large structures are frequently accessed during the scanning phase. The first is the "lookup table", which maps words in a subject sequence to positions in the query. The second is the "diag-array", which tracks how far BLAST has already extended word hits on any given diagonal; its size scales with the query length. The scanning phase is a large fraction of the time of most BLAST searches, so these structures must be accessed quickly. Contemporary CPUs typically communicate with main memory through several levels of cache, called a "memory hierarchy". For example, the L1 cache is the smallest and has the lowest latency; the L2 cache is larger but slower. On a machine with an Intel Xeon CPU, the L1 cache might be around 16 kB and the L2 cache can range in size from 0.5-4 MB. If the CPU does not find data or an instruction in the cache, it must fetch it from main memory; a "cache miss". Performance could be improved by making the lookup table and diag-array small enough to fit into L2 cache, still leaving room for instructions and other data.

In order to be specific, the discussion in the next two paragraphs is limited to a BLASTX search, which translates a nucleotide query in six frames (three frames on each strand) and compares it to a protein database.

The lookup table contains a long array (the "backbone"), with each cell mapping to a unique word. The lookup table translates each residue type to a number between 1 and 24, so a three-letter word maps to an integer between 1 and 24 3 . For a three-letter word, an array of 32768 (32 3 ) cells allows a quick calculation of the offset into the backbone while scanning the database for word matches. Each cell of the backbone consists of four integers. The first integer specifies how many times that word appears in the query; the other three can have one of two functions. For three or fewer occurrences, the three integers simply specify the positions of the word in the query. If there are more than three occurrences, however, the integers are an index into another array containing the positions of the word in the query. The total memory occupied by the backbone is 16 bytes × 32768, or about 524 kB. Finally, there is a bit vector occupying 4096 bytes (32768/8). The corresponding bit is set in the bit vector for backbone cells containing entries. For a short query, where the backbone may be sparsely populated, this allows a quick check whether a cell contains any information.

A BLASTX query of N nucleotides becomes twice as long when it is represented as six protein sequences. The diagarray consumes one four-byte integer per letter in the query. An estimate of the total memory occupied by the lookup table backbone and the diag-array, in bytes, for a nucleotide query of length N is:
528 384 8 , + N
For a query of N = 50 k, this is close to a million bytes, already the total size of L2 cache in many computers used for BLAST searching. Modifications to these structures might permit larger queries, but for contigs and chromosomes the structures would still overflow the L2 cache. To overcome this, the query is split into smaller overlapping pieces for the scanning phase of the search. BLAST then merges the results and aligns the entire query during the trace-back phase, obtaining the same results as a search that was not split. Splitting the query has an additional advantage; since the sub-query used during the scanning phase is of bounded length, it is possible to use a smaller data type in the lookup table (specifically, a two byte rather than a four byte integer). This reduces the first term in the above equation from 528,384 to 266,240 bytes.

The final phase of the BLAST search, the trace-back, processes the preliminary matches, producing an alignment with insertions and deletions. Additionally, heuristic parameters may be assigned a more sensitive value, ambiguities in a nucleotide database sequence are resolved, and the composition of the subject sequences may be taken into account when calculating expect values. Some subject sequences must be retrieved again for this calculation, but since the preliminary phase finds the rough extent of any alignment, the entire sequence is often not needed. This is most important for short queries searched against a database of much longer sequences. Only part of the subject sequences, when appropriate, is now retrieved, and performance results are presented under "Partial subject sequence retrieval" below.

Results and discussion

First, we introduce a set of BLAST command-line applications built with the software library discussed above. Then, we present an example use of database masking as well as two performance analyses that demonstrate improvements in search time: searches with very long queries and searches of chromosome-sized database sequences. For each performance analysis, we prepared a baseline application that disables the new feature being tested. Finally, we discuss an example of retrieving subject sequences from an arbitrary source.

A SUSE Linux machine with an Intel Xeon 3.6 GHz CPU, 16 kB of L1 cache, 1 MB of L2 cache, and 8 GB of RAM, provided data for the comparisons described here.

BLAST+ command-line applications

New command-line applications have been developed using the NCBI C++ toolkit, and they are referred to as the BLAST+ command-line applications (or BLAST+ applications). Extensive documentation about the different command-line options is available [17], so only general comments about the interface are presented here. The NCBI C++ toolkit argument parser permitted the use of multi-letter command-line arguments. New BLAST+ command-line applications were introduced, dependent upon the molecule types of the query and subject sequences. For example, there is a "blastx" application that translates a nucleotide query and compares it to a protein database, and a "blastn" application that compares a nucleotide query to a nucleotide database. The command-line options and help messages are specific to each application. In contrast, the current C toolkit command-line application ("blastall") presents usage instructions about nucleotide match and mismatch scores, needed only for BLASTN, even if the user wants to perform a BLASTX search. Users also need to optimize for different tasks within a single command-line application. For example, MEGABLAST compares a nucleotide query to a nucleotide database, but is optimized for closely related sequences (e.g., searching for sequencing errors), using a large word size and a linear gap penalty. BLASTN, on the other hand, is the traditional nucleotide-nucleotide search program and uses a smaller word size and affine gapping by default. The concept of a "task" allows a user to optimize the search for different scenarios within one application. Setting the task for the blastn application changes the default value of a number of command-line arguments, such as the word size, but also the default scoring parameters for insertions, deletions, and mismatches. These values are changed to typical values that would be used with the selected task. For the MEGABLAST task, the nucleotide match and mismatch values are 1 and -2, as this corresponds to 95% identity matches. In contrast, for BLASTN and DISCONTIGUOUS MEGABLAST, the values are 2 and -3 as they correspond to 85% identity [18].

Power users of BLAST often have a specially crafted set of command-line options that they find useful for their particular task. However, lacking a method to save these, they must write scripts or simply re-type them for each search. The BLAST+ applications can write the query, database, and command-line options for a BLAST search into a "strategy" file. A user may then rerun a set of commands by specifying the strategy file, though a new query and database can be specified with the command-line. This file is currently written as ASN.1 (Abstract Syntax Notation, a structured language similar to XML), but an XML option could be added in the future. Users can also upload this file to the NCBI BLAST web site to populate a BLAST search form, or download a strategy file for a search performed at the NCBI BLAST web site.

The BLAST+ applications have a number of new features. A GI or accession may be used as the query, with the actual sequence automatically retrieved from a BLAST database (the sequence must be available in a BLAST database) or from GenBank. The applications can send a search to NCBI servers as well as locally search a set of queries against a set of FASTA subject sequences [17].

Tables listing the command-line options, as well as their types and defaults, were provided as additional file 1 for this article.

Database masking

Applying masking information to the BLAST database rather than the query will improve the workflow for BLAST users. A specialized tool, such as WindowMasker [19] or RepeatMasker [10], can provide masking information for a single-species database when it is created, and it becomes unnecessary to mask every query. Adding masking information to a BLAST database is a two step process. A file containing masking intervals in either XML or ASN.1 format is first produced, and then the information is added to the BLAST database. The NCBI C++ toolkit provides tools to produce this information for seg [20], dust [21], and WindowMasker [19]. Users may also provide intervals for algorithms not supported by the NCBI C++ toolkit; see the BLAST+ manual [17] for further information on how to produce a masked database. Currently, database masking is only available in soft-masking mode.

To test the performance of database masking, 163 human ESTs from UniGene cluster 235935 were searched against the build 36.1 reference assembly of the human genome [22]. RepeatMasker processed the EST queries, producing FASTA files with repeats identified in lower-case. Repeat-Masker also processed the human genome FASTA files, locations of repeats were produced from that data, and those locations were then added as masking information to the BLAST database. Two sets of searches were run. One used the lower-case query masking to filter out interspersed repeats; the other used the database masking to do the same. Alignments with a score of 100 or more were retained. Table 1 presents the results, which indicate that differences in query masking with RepeatMasker caused extra matches. For example GI 14400848 is only 145 bases long and is not masked by RepeatMasker at all, but the portion of the genome it matches is masked. For GI 13529935 the last 78 bases are not masked, but the portion of the genome it matches is masked by RepeatMasker.

Currently, database masking is not supported for searches of translated database sequences (i.e., tblastn and tblastx), but it will be supported in the near future.

Database masking is not a new concept. Kent [13] mentions cases where BLAT users might find repeat masking of the database useful. Morgulis et al. [23] also allow users to apply soft-masking to their database. In both of these cases, it is not simple to turn the masking on or off or to switch the type of masking (e.g., from RepeatMasker to WindowMasker). The implementation presented here allows this flexibility.

Query splitting

Breaking longer queries into smaller pieces for processing can lead to significantly shorter search times. At the same time, splitting the query into pieces makes it possible to guarantee that the query length is always bounded, allowing the use of smaller data types in the lookup table. Use of smaller data types with a BLASTP search (protein-protein) shows no improvement for sequences under 500 residues, but performance increases by up to 2% as the sequence length increases to 8000 residues. Use of a smaller data type never makes performance worse, so it is used in the tests described in this section.

BLAST searches of differently-sized chunks of zebra fish chromosome 2 [Genbank:NC_007113.2] against a set of human proteins were performed to test the query splitting implementation. A baseline blastx application that does not split the query was prepared. Figure 2 presents the speedup for these searches, with speedup defined as (T baseline /T blastx ) -1. Query splitting decreases the search time for queries longer than 20 kbases, and the improvement continues with increasing query length. The Cachegrind memory profiling tool [24] confirmed a smaller number of cache misses with query splitting. Figure 3 presents those results. Figures 2 and 3 reflect an expect value cutoff of 1.0e-6.

Cameron et al. [14] replaced the BLAST lookup table with a DFA (Deterministic Finite Automaton) to improve the cache behavior. They reported a 10-15% reduction in search time for BLASTP (protein-protein) searches. Most proteins are too short to split, so no significant BLASTP improvements were apparent in the work presented here. This work emphasized improving the worst-case behavior typically seen with very long nucleotide queries. The query splitting approach does not preclude the use of a DFA or some other optimization instead of a lookup table.

Retrieving subject sequences from an arbitrary source

An Abstract Data Type (ADT) supplies the subject sequences to be searched in the new BLAST code. This abstraction avoids coupling the BLAST engine to a particular database format. It permits a search of sequences in the "Short Read Archive" (SRA) at the NCBI through the SRA Software Development Kit [25]. An SRA BLAST web page accessible from the BLAST web site [11] was also created.

Future development

Future developments include adding hard-masking support for databases, and making database masking available for programs with translated database sequences (tblastn and tblastx). At this point, only the scanning phase of the BLAST search is multi-threaded; we also plan to make the trace-back phase multi-threaded.

Conclusions

We have reported on a new modular software library for BLAST. The design allows the addition of features that greatly benefit performance, such as query splitting and partial retrieval of subject sequences. It also allows the replacement of the lookup Speedup of BLASTX searches for differently sized queries with and without query splitting Figure 2 Speedup of BLASTX searches for differently sized queries with and without query splitting. Different sized pieces of [Genbank:NC_007113.2] were searched against a set of human proteins. The query length in kbases is on the x-axis, with a log scale. On the y-axis is the fractional speedup, which is defined as (T baseline /T blastx ) -1. Three searches were performed with both the baseline and the blastx applications (for each data point), and the lowest time for each application was used. We also described a new set of BLAST command-line applications. The applications have a new, more logical organization that groups together similar types of searches in one application. The concept of a task allows a user to specify an optimal parameter set for a given task. Strategy files were also introduced, allowing a user to record parameters of a search in order to later rerun it in standalone mode or at the NCBI web site. Scatter plot of MEGABLAST search times with and without partial retrieval Figure 4 Scatter plot of MEGABLAST search times with and without partial retrieval. 163 human ESTs from UniGene cluster 235935 were searched against all human chromosomes [22]. On the x-axis are times for the baseline application; on the y-axis are times for the new blastn application. Sequences with the best improvement are those furthest to the right, and they also matched the largest number of subject sequences. A word size of 24 was used for the runs as well as database masking with RepeatMasker. Three searches were done with both the baseline and blastn application for each data point, and the lowest time for each application was used.

Additional file 1

Publish with Bio Med Central and every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours -you keep the copyright Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.

Table 1 :
1Comparison of query versus database masking.Type of masking
Number of alignments found
GIs of extra sequences found

Query
387
13529935, 14400848, 14430244, 14430457

Database
383

table with another design, so that new implementations can easily be added. An indexed version of MEGABLAST[23] was implemented using these libraries. The new library also supports a framework for retrieving subject sequences from arbitrary data sources. This framework, an Abstract Data Type (ADT), allows the use of different modules to read the BLAST databases in the NCBI C++ and the C toolkits. It is possible to write a new module to supply subject sequences to the BLAST engine using this ADT [16] without any modifications of the BLAST algorithm code. An ADT implementation has been written to support production searches of SRA sequences at the NCBI. L2 data cache misses for BLASTX searches with and without query splitting. Cache misses were measured by Cachegrind[24] and only misses reading from the cache are shown. On the x-axis are different query lengths in kbases. The number of L2 cache misses is shown on the y-axis. The top line is for the baseline application without query splitting, the bottom line is for the blastx application. The queries are different sized pieces of [Genbank:NC_007113.2] searched against the set of human proteins used forFigure 2.L2 data cache misses for BLASTX searches with and without
query splitting
Figure 3
0

50,000,000

100,000,000

150,000,000

200,000,000

250,000,000

300,000,000

10
15
20
25
30
35
40
45
50
55
60
65
70
75
80

L2 data read misses

Query length (kbases)

Eight tables list the command-line application options, as well as their types, default values, and a short explanation.The first table has information common to the search applications blastn, blastp, blastx, tblastn, and tblastx. The next five tables describe options for those applications. The last two tables list the options for makeblastdb (used to build a blast database) and blastdbcmd (used to read a database). Click here for file [http://www.biomedcentral.com/content/supplementary/1471-2105-10-421-S1.PDF]
(page number not for citation purposes)
AcknowledgementsA number of people contributed to this project. Richa Agarwala, Alejandro Schaffer, and Mike DiCuccio offered ideas and feedback. Mike Gertz, Aleksandr Morgulis, and Ilya Dondoshansky contributed some of the code used in the core of BLAST. Denis Vakatov, Aaron Ucko and other members of the NCBI C++ toolkit group offered assistance as well as the C++ toolkit used to build BLAST+. Eugene Yaschenko, Kurt Rodarmer and Ty Roach provided help in using the NCBI SRA Software Development Toolkit. David Lipman and Jim Ostell originally suggested the need for a rewritten version of BLAST and provided encouragement and feedback. Greg Boratyn, Maureen Madden and John Spouge read the manuscript and offered helpful suggestions.This research was supported by the Intramural Research Program of the NIH, National Library of Medicine. Funding to pay the Open Access publication charges for this article was provided by the National Institutes of Health.Availability and requirementsBLAST is Public Domain software[26]. The latest version of BLAST can be retrieved from ftp:// ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST. This software was implemented with the C and C++ programming languages and was tested under Microsoft Windows, Linux, and Mac OS X. There are no restrictions on use by non-academics. Query files and BLAST databases used for tests are available at ftp://ftp.ncbi.nih.gov/blast/ demo/bmc.Authors' contributionsAll authors participated in the design and coding of the software. TLM drafted the manuscript and the other authors provided feedback. All authors read and approved the final version of the manuscript.Additional material
Basic local alignment search tool. S Altschul, W Gish, W Miller, E Myers, D Lipman, J Mol Biol. 2153Altschul S, Gish W, Miller W, Myers E, Lipman D: Basic local align- ment search tool. J Mol Biol 1990, 215(3):403-410.

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. S Altschul, T Madden, A Schäffer, J Zhang, Z Zhang, W Miller, D Lipman, Nucleic Acids Res. 2517Altschul S, Madden T, Schäffer A, Zhang J, Zhang Z, Miller W, Lipman D: Gapped BLAST and PSI-BLAST: a new generation of pro- tein database search programs. Nucleic Acids Res 1997, 25(17):3389-3402.

. Ncbi C Toolkit, NCBI C toolkit [http://www.ncbi.nlm.nih.gov/IEB/ToolBox/SDK DOCS/INDEX.HTML]

Altschul S: Protein sequence similarity searches using patterns as seeds. Z Zhang, A Schäffer, W Miller, T Madden, D Lipman, E Koonin, Nucleic Acids Res. 2617Zhang Z, Schäffer A, Miller W, Madden T, Lipman D, Koonin E, Alts- chul S: Protein sequence similarity searches using patterns as seeds. Nucleic Acids Res 1998, 26(17):3986-3990.

IMPALA: matching a protein sequence against a collection of PSI-BLAST-constructed position-specific score matrices. A Schäffer, Y Wolf, C Ponting, E Koonin, L Aravind, S Altschul, Bioinformatics. 1512Schäffer A, Wolf Y, Ponting C, Koonin E, Aravind L, Altschul S: IMPALA: matching a protein sequence against a collection of PSI-BLAST-constructed position-specific score matrices. Bioinformatics 1999, 15(12):1000-1011.

Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. A Schäffer, L Aravind, T Madden, S Shavirin, J Spouge, Y Wolf, E Koonin, S Altschul, Nucleic Acids Res. 2914Schäffer A, Aravind L, Madden T, Shavirin S, Spouge J, Wolf Y, Koonin E, Altschul S: Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 2001, 29(14):2994-3005.

A greedy algorithm for aligning DNA sequences. Z Zhang, S Schwartz, L Wagner, W Miller, J Comput Biol. 71-2Zhang Z, Schwartz S, Wagner L, Miller W: A greedy algorithm for aligning DNA sequences. J Comput Biol 7(1-2):203-214.

. A/G Blast, A/G BLAST [http://www.apple.com/downloads/macosx/ math_science/agblast.html]

Initial sequencing and comparative analysis of the mouse genome. R Waterston, K Lindblad-Toh, E Birney, J Rogers, J Abril, P Agarwal, R Agarwala, R Ainscough, M Alexandersson, P An, Nature. 4206915Waterston R, Lindblad-Toh K, Birney E, Rogers J, Abril J, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, et al.: Initial sequencing and comparative analysis of the mouse genome. Nature 2002, 420(6915):520-562.

RepeatMasker Web site. RepeatMasker Web site [http://www.repeatmasker.org/]

NCBI BLAST web site. NCBI BLAST web site [http://blast.ncbi.nlm.nih.gov/Blast.cgi]

Madden T: NCBI BLAST: a better web interface. M Johnson, I Zaretskaya, Y Raytselis, Y Merezhuk, S Mcginnis, Nucleic Acids Res. 36Web Server issueJohnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Mad- den T: NCBI BLAST: a better web interface. Nucleic Acids Res 2008, 36(Web Server issue):W5-9.

BLAT--the BLAST-like alignment tool. W Kent, Genome Res. 124Kent W: BLAT--the BLAST-like alignment tool. Genome Res 2002, 12(4):656-664.

A deterministic finite automaton for faster protein hit detection in BLAST. M Cameron, H Williams, A Cannane, NCBI C++ toolkit documentation. 13Cameron M, Williams H, Cannane A: A deterministic finite automaton for faster protein hit detection in BLAST. J Com- put Biol 2006, 13(4):965-978. 15. NCBI C++ toolkit documentation [http:// www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=toolkit]

BLAST+ Command Line Applications User Manual. BLAST+ Command Line Applications User Manual [http:// www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=helpblast]

Improved sensitivity of nucleic acid database searches using application-specific scoring matrices. D J States, W Gish, S F Altschul, METHODS: A Companion to Methods in Enzymology. 3States DJ, Gish W, Altschul SF: Improved sensitivity of nucleic acid database searches using application-specific scoring matrices. METHODS: A Companion to Methods in Enzymology 1991, 3:66-70.

WindowMasker: window-based masker for sequenced genomes. A Morgulis, E Gertz, A Schäffer, R Agarwala, Bioinformatics. 222Morgulis A, Gertz E, Schäffer A, Agarwala R: WindowMasker: win- dow-based masker for sequenced genomes. Bioinformatics 2006, 22(2):134-141.

Analysis of compositionally biased regions in sequence databases. Computer Methods for Macromolecular Sequence Analysis. J C Wootton, S Federhen, 266Wootton JC, Federhen S: Analysis of compositionally biased regions in sequence databases. Computer Methods for Macromo- lecular Sequence Analysis 1996, 266:554-571.

A fast and symmetric DUST implementation to mask low-complexity DNA sequences. A Morgulis, E Gertz, A Schäffer, R Agarwala, J Comput Biol. 135Morgulis A, Gertz E, Schäffer A, Agarwala R: A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J Comput Biol 2006, 13(5):1028-1040.

Database indexing for production MegaBLAST searches. A Morgulis, G Coulouris, Y Raytselis, T Madden, R Agarwala, A Schäffer, Bioinformatics. 2416Morgulis A, Coulouris G, Raytselis Y, Madden T, Agarwala R, Schäffer A: Database indexing for production MegaBLAST searches. Bioinformatics 2008, 24(16):1757-1764.

NCBI SRA Software Development Kit. Cachegrind [http://valgrind.org/docs/manual/cg-manual.html] 25. NCBI SRA Software Development Kit [http:www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=soft ware&m=software&s=software]

PUBLIC DOMAIN NOTICE for NCBI. PUBLIC DOMAIN NOTICE for NCBI [http:// www.ncbi.nlm.nih.gov/bookshelf/ br.fcgi?book=toolkit&part=toolkit.fm#A3]