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2.  Sensitive and fast mapping of di-base encoded reads 
Bioinformatics  2011;27(14):1915-1921.
Motivation: Discovering variation among high-throughput sequenced genomes relies on efficient and effective mapping of sequence reads. The speed, sensitivity and accuracy of read mapping are crucial to determining the full spectrum of single nucleotide variants (SNVs) as well as structural variants (SVs) in the donor genomes analyzed.
Results: We present drFAST, a read mapper designed for di-base encoded ‘color-space’ sequences generated with the AB SOLiD platform. drFAST is specially designed for better delineation of structural variants, including segmental duplications, and is able to return all possible map locations and underlying sequence variation of short reads within a user-specified distance threshold. We show that drFAST is more sensitive in comparison to all commonly used aligners such as Bowtie, BFAST and SHRiMP. drFAST is also faster than both BFAST and SHRiMP and achieves a mapping speed comparable to Bowtie.
Availability: The source code for drFAST is available at http://drfast.sourceforge.net
Contact: calkan@u.washington.edu
doi:10.1093/bioinformatics/btr303
PMCID: PMC3129524  PMID: 21586516
3.  Dissect: detection and characterization of novel structural alterations in transcribed sequences 
Bioinformatics  2012;28(12):i179-i187.
Motivation: Computational identification of genomic structural variants via high-throughput sequencing is an important problem for which a number of highly sophisticated solutions have been recently developed. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention.
In this article, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model; a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect (DIScovery of Structural Alteration Event Containing Transcripts), which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs.
Results: We tested Dissect on simulated transcripts altered via structural events, as well as assembled RNA-Seq contigs from human prostate cancer cell line C4-2. Our results indicate that Dissect has high sensitivity and specificity in identifying structural alteration events in simulated transcripts as well as uncovering novel structural alterations in cancer transcriptomes.
Availability: Dissect is available for public use at: http://dissect-trans.sourceforge.net
Contact: denizy@mit.edu; fhach@cs.sfu.ca; cenk@cs.sfu.ca
doi:10.1093/bioinformatics/bts214
PMCID: PMC3371846  PMID: 22689759
4.  Optimally discriminative subnetwork markers predict response to chemotherapy 
Bioinformatics  2011;27(13):i205-i213.
Motivation: Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems.
Results: We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy.
Availability: The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html
Contact: cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com
doi:10.1093/bioinformatics/btr245
PMCID: PMC3117373  PMID: 21685072
5.  Detection and characterization of novel sequence insertions using paired-end next-generation sequencing 
Bioinformatics  2010;26(10):1277-1283.
Motivation: In the past few years, human genome structural variation discovery has enjoyed increased attention from the genomics research community. Many studies were published to characterize short insertions, deletions, duplications and inversions, and associate copy number variants (CNVs) with disease. Detection of new sequence insertions requires sequence data, however, the ‘detectable’ sequence length with read-pair analysis is limited by the insert size. Thus, longer sequence insertions that contribute to our genetic makeup are not extensively researched.
Results: We present NovelSeq: a computational framework to discover the content and location of long novel sequence insertions using paired-end sequencing data generated by the next-generation sequencing platforms. Our framework can be built as part of a general sequence analysis pipeline to discover multiple types of genetic variation (SNPs, structural variation, etc.), thus it requires significantly less-computational resources than de novo sequence assembly. We apply our methods to detect novel sequence insertions in the genome of an anonymous donor and validate our results by comparing with the insertions discovered in the same genome using various sources of sequence data.
Availability: The implementation of the NovelSeq pipeline is available at http://compbio.cs.sfu.ca/strvar.htm
Contact:eee@gs.washington.edu; cenk@cs.sfu.ca
doi:10.1093/bioinformatics/btq152
PMCID: PMC2865866  PMID: 20385726
6.  Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery 
Bioinformatics  2010;26(12):i350-i357.
Recent years have witnessed an increase in research activity for the detection of structural variants (SVs) and their association to human disease. The advent of next-generation sequencing technologies make it possible to extend the scope of structural variation studies to a point previously unimaginable as exemplified by the 1000 Genomes Project. Although various computational methods have been described for the detection of SVs, no such algorithm is yet fully capable of discovering transposon insertions, a very important class of SVs to the study of human evolution and disease. In this article, we provide a complete and novel formulation to discover both loci and classes of transposons inserted into genomes sequenced with high-throughput sequencing technologies. In addition, we also present ‘conflict resolution’ improvements to our earlier combinatorial SV detection algorithm (VariationHunter) by taking the diploid nature of the human genome into consideration. We test our algorithms with simulated data from the Venter genome (HuRef) and are able to discover >85% of transposon insertion events with precision of >90%. We also demonstrate that our conflict resolution algorithm (denoted as VariationHunter-CR) outperforms current state of the art (such as original VariationHunter, BreakDancer and MoDIL) algorithms when tested on the genome of the Yoruba African individual (NA18507).
Availability: The implementation of algorithm is available at http://compbio.cs.sfu.ca/strvar.htm.
Contact: eee@gs.washington.edu; cenk@cs.sfu.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq216
PMCID: PMC2881400  PMID: 20529927
7.  PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes 
Bioinformatics  2010;26(13):1608-1615.
Motivation: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program.
Results: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions.
Availability: http://www.psort.org/psortb (download open source software or use the web interface).
Contact: psort-mail@sfu.ca
Supplementary Information: Supplementary data are availableat Bioinformatics online.
doi:10.1093/bioinformatics/btq249
PMCID: PMC2887053  PMID: 20472543
8.  A partition function algorithm for interacting nucleic acid strands 
Bioinformatics  2009;25(12):i365-i373.
Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact.
Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and eukaryotic cells show that such ncRNAs usually bind to their target mRNA to regulate the translation of corresponding genes. The specificity of these interactions depends on the stability of intermolecular and intramolecular base pairing. While methods like deep sequencing allow to discover an ever increasing set of ncRNAs, there are no high-throughput methods available to detect their associated targets. Hence, there is an increasing need for precise computational target prediction. In order to predict base-pairing probability of any two bases in interacting nucleic acids, it is necessary to compute the interaction partition function over the whole ensemble. The partition function is a scalar value from which various thermodynamic quantities can be derived. For example, the equilibrium concentration of each complex nucleic acid species and also the melting temperature of interacting nucleic acids can be calculated based on the partition function of the complex.
Results: We present a model for analyzing the thermodynamics of two interacting nucleic acid strands considering the most general type of interactions studied in the literature. We also present a corresponding dynamic programming algorithm that computes the partition function over (almost) all physically possible joint secondary structures formed by two interacting nucleic acids in O(n6) time. We verify the predictive power of our algorithm by computing (i) the melting temperature for interacting RNA pairs studied in the literature and (ii) the equilibrium concentration for several variants of the OxyS–fhlA complex. In both experiments, our algorithm shows high accuracy and outperforms competitors.
Availability: Software and web server is available at http://compbio.cs.sfu.ca/taverna/pirna/
Contact: cenk@cs.sfu.ca; backofen@informatik.uni-freiburg.de
Supplementary information: Supplementary data are avaliable at Bioinformatics online.
doi:10.1093/bioinformatics/btp212
PMCID: PMC2687966  PMID: 19478011
9.  Optimal pooling for genome re-sequencing with ultra-high-throughput short-read technologies 
Bioinformatics  2008;24(13):i32-i40.
New generation sequencing technologies offer unique opportunities and challenges for re-sequencing studies. In this article, we focus on re-sequencing experiments using the Solexa technology, based on bacterial artificial chromosome (BAC) clones, and address an experimental design problem. In these specific experiments, approximate coordinates of the BACs on a reference genome are known, and fine-scale differences between the BAC sequences and the reference are of interest. The high-throughput characteristics of the sequencing technology makes it possible to multiplex BAC sequencing experiments by pooling BACs for a cost-effective operation. However, the way BACs are pooled in such re-sequencing experiments has an effect on the downstream analysis of the generated data, mostly due to subsequences common to multiple BACs. The experimental design strategy we develop in this article offers combinatorial solutions based on approximation algorithms for the well-known max n-cut problem and the related max n-section problem on hypergraphs. Our algorithms, when applied to a number of sample cases give more than a 2-fold performance improvement over random partitioning.
Contact:cenk@cs.sfu.ca
doi:10.1093/bioinformatics/btn173
PMCID: PMC2718651  PMID: 18586730
10.  Biomolecular network motif counting and discovery by color coding 
Bioinformatics  2008;24(13):i241-i249.
Protein–protein interaction (PPI) networks of many organisms share global topological features such as degree distribution, k-hop reachability, betweenness and closeness. Yet, some of these networks can differ significantly from the others in terms of local structures: e.g. the number of specific network motifs can vary significantly among PPI networks.
Counting the number of network motifs provides a major challenge to compare biomolecular networks. Recently developed algorithms have been able to count the number of induced occurrences of subgraphs with k≤ 7 vertices. Yet no practical algorithm exists for counting non-induced occurrences, or counting subgraphs with k≥ 8 vertices. Counting non-induced occurrences of network motifs is not only challenging but also quite desirable as available PPI networks include several false interactions and miss many others.
In this article, we show how to apply the ‘color coding’ technique for counting non-induced occurrences of subgraph topologies in the form of trees and bounded treewidth subgraphs. Our algorithm can count all occurrences of motif G′ with k vertices in a network G with n vertices in time polynomial with n, provided k=O(log n). We use our algorithm to obtain ‘treelet’ distributions for k≤ 10 of available PPI networks of unicellular organisms (Saccharomyces cerevisiae Escherichia coli and Helicobacter Pyloris), which are all quite similar, and a multicellular organism (Caenorhabditis elegans) which is significantly different. Furthermore, the treelet distribution of the unicellular organisms are similar to that obtained by the ‘duplication model’ but are quite different from that of the ‘preferential attachment model’. The treelet distribution is robust w.r.t. sparsification with bait/edge coverage of 70% but differences can be observed when bait/edge coverage drops to 50%.
Contact:cenk@cs.sfu.ca
doi:10.1093/bioinformatics/btn163
PMCID: PMC2718641  PMID: 18586721

Results 1-10 (10)