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1.  The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation 
Bioinformatics  2014;30(13):1920-1922.
Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines.
Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4071205  PMID: 24618464
2.  Comprehensive Assessment of Potential Multiple Myeloma Immunoglobulin Heavy Chain V-D-J Intraclonal Variation Using Massively Parallel Pyrosequencing 
Oncotarget  2012;3(4):502-513.
Multiple myeloma (MM) is characterized by the accumulation of malignant plasma cells (PCs) in the bone marrow (BM). MM is viewed as a clonal disorder due to lack of verified intraclonal sequence diversity in the immunoglobulin heavy chain variable region gene (IGHV). However, this conclusion is based on analysis of a very limited number of IGHV subclones and the methodology employed did not permit simultaneous analysis of the IGHV repertoire of non-malignant PCs in the same samples. Here we generated genomic DNA and cDNA libraries from purified MM BMPCs and performed massively parallel pyrosequencing to determine the frequency of cells expressing identical IGHV sequences. This method provided an unprecedented opportunity to interrogate the presence of clonally related MM cells and evaluate the IGHV repertoire of non-MM PCs. Within the MM sample, 37 IGHV genes were expressed, with 98.9% of all immunoglobulin sequences using the same IGHV gene as the MM clone and 83.0% exhibiting exact nucleotide sequence identity in the IGHV and heavy chain complementarity determining region 3 (HCDR3). Of interest, we observed in both genomic DNA and cDNA libraries 48 sets of identical sequences with single point mutations in the MM clonal IGHV or HCDR3 regions. These nucleotide changes were suggestive of putative subclones and therefore were subjected to detailed analysis to interpret: 1) their legitimacy as true subclones; and 2) their significance in the context of MM. Finally, we report for the first time the IGHV repertoire of normal human BMPCs and our data demonstrate the extent of IGHV repertoire diversity as well as the frequency of clonally-related normal BMPCs. This study demonstrates the power and potential weaknesses of in-depth sequencing as a tool to thoroughly investigate the phylogeny of malignant PCs in MM and the IGHV repertoire of normal BMPCs.
PMCID: PMC3380583  PMID: 22522905
IGHV; multiple myeloma; heterogeneity; massively parallel sequencing
3.  Deep Sequence Analysis of Non-Small Cell Lung Cancer: Integrated Analysis of Gene Expression, Alternative Splicing, and Single Nucleotide Variations in Lung Adenocarcinomas with and without Oncogenic KRAS Mutations 
KRAS mutations are highly prevalent in non-small cell lung cancer (NSCLC), and tumors harboring these mutations tend to be aggressive and resistant to chemotherapy. We used next-generation sequencing technology to identify pathways that are specifically altered in lung tumors harboring a KRAS mutation. Paired-end RNA-sequencing of 15 primary lung adenocarcinoma tumors (8 harboring mutant KRAS and 7 with wild-type KRAS) were performed. Sequences were mapped to the human genome, and genomic features, including differentially expressed genes, alternate splicing isoforms and single nucleotide variants, were determined for tumors with and without KRAS mutation using a variety of computational methods. Network analysis was carried out on genes showing differential expression (374 genes), alternate splicing (259 genes), and SNV-related changes (65 genes) in NSCLC tumors harboring a KRAS mutation. Genes exhibiting two or more connections from the lung adenocarcinoma network were used to carry out integrated pathway analysis. The most significant signaling pathways identified through this analysis were the NFκB, ERK1/2, and AKT pathways. A 27 gene mutant KRAS-specific sub network was extracted based on gene–gene connections from the integrated network, and interrogated for druggable targets. Our results confirm previous evidence that mutant KRAS tumors exhibit activated NFκB, ERK1/2, and AKT pathways and may be preferentially sensitive to target therapeutics toward these pathways. In addition, our analysis indicates novel, previously unappreciated links between mutant KRAS and the TNFR and PPARγ signaling pathways, suggesting that targeted PPARγ antagonists and TNFR inhibitors may be useful therapeutic strategies for treatment of mutant KRAS lung tumors. Our study is the first to integrate genomic features from RNA-Seq data from NSCLC and to define a first draft genomic landscape model that is unique to tumors with oncogenic KRAS mutations.
PMCID: PMC3356053  PMID: 22655260
transcriptome sequencing; RNA-Seq; KRAS mutation; NSCLC; bioinformatics; network analysis; data integration and computational methods
4.  TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data 
Bioinformatics  2011;28(2):277-278.
Summary: TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways.
Availability and implementation: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website:
Supplementary information: Supplementary data are provided at Bioinformatics online.
PMCID: PMC3259432  PMID: 22088845
5.  A novel bioinformatics pipeline for identification and characterization of fusion transcripts in breast cancer and normal cell lines 
Nucleic Acids Research  2011;39(15):e100.
SnowShoes-FTD, developed for fusion transcript detection in paired-end mRNA-Seq data, employs multiple steps of false positive filtering to nominate fusion transcripts with near 100% confidence. Unique features include: (i) identification of multiple fusion isoforms from two gene partners; (ii) prediction of genomic rearrangements; (iii) identification of exon fusion boundaries; (iv) generation of a 5′–3′ fusion spanning sequence for PCR validation; and (v) prediction of the protein sequences, including frame shift and amino acid insertions. We applied SnowShoes-FTD to identify 50 fusion candidates in 22 breast cancer and 9 non-transformed cell lines. Five additional fusion candidates with two isoforms were confirmed. In all, 30 of 55 fusion candidates had in-frame protein products. No fusion transcripts were detected in non-transformed cells. Consideration of the possible functions of a subset of predicted fusion proteins suggests several potentially important functions in transformation, including a possible new mechanism for overexpression of ERBB2 in a HER-positive cell line. The source code of SnowShoes-FTD is provided in two formats: one configured to run on the Sun Grid Engine for parallelization, and the other formatted to run on a single LINUX node. Executables in PERL are available for download from our web site:
PMCID: PMC3159479  PMID: 21622959

Results 1-5 (5)