XORRO: Rapid Paired-End Read Overlapper
Russell J. Dickson, Gregory B. Gloor
(Submitted on 16 Apr 2013)

Background: Computational analysis of next-generation sequencing data is outpaced by data generation in many cases. In one such case, paired-end reads can be produced from the Illumina sequencing method faster than they can be overlapped by downstream analysis. The advantages in read length and accuracy provided by overlapping paired-end reads demonstrates the necessity for software to efficiently solve this problem.
Results: XORRO is an extremely efficient paired-end read overlapping program. XORRO can overlap millions of short paired-end reads in a few minutes. It uses 64-bit registers with a two bit alphabet to represent sequences and does comparisons using low-level logical operations like XOR, AND, bitshifting and popcount.
Conclusions: As of the writing of this manuscript, XORRO provides the fastest solution to the paired-end read overlap problem. XORRO is available for download at: sourceforge.net/projects/xorro-overlap/

High-speed and accurate color-space short-read alignment with CUSHAW2
Yongchao Liu, Bernt Popp, Bertil Schmidt
(Submitted on 17 Apr 2013)

Summary: We present an extension of CUSHAW2 for fast and accurate alignments of SOLiD color-space short-reads. Our extension introduces a double-seeding approach to improve mapping sensitivity, by combining maximal exact match seeds and variable-length seeds derived from local alignments. We have compared the performance of CUSHAW2 to SHRiMP2 and BFAST by aligning both simulated and real color-space mate-paired reads to the human genome. The results show that CUSHAW2 achieves comparable or better alignment quality compared to SHRiMP2 and BFAST at an order-of-magnitude faster speed and significantly smaller peak resident memory size. Availability: CUSHAW2 and all simulated datasets are available at this http URL Contact: liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de

The Convergence of eQTL Mapping, Heritability Estimation and Polygenic Modeling: Emerging Spectrum of Risk Variation in Bipolar Disorder
Eric R. Gamazon, Hae Kyung Im, Chunyu Liu, Members of the Bipolar Disorder Genome Study (BiGS) Consortium, Dan L. Nicolae, Nancy J. Cox
(Submitted on 25 Mar 2013)

It is widely held that a substantial genetic component underlies Bipolar Disorder (BD) and other neuropsychiatric disease traits. Recent efforts have been aimed at understanding the genetic basis of disease susceptibility, with genome-wide association studies (GWAS) unveiling some promising associations. Nevertheless, the genetic etiology of BD remains elusive with a substantial proportion of the heritability – which has been estimated to be 80% based on twin and family studies – unaccounted for by the specific genetic variants identified by large-scale GWAS. Furthermore, functional understanding of associated loci generally lags discovery. Studies we report here provide considerable support to the claim that substantially more remains to be gained from GWAS on the genetic mechanisms underlying BD susceptibility, and that a large proportion of the variation in disease risk may be uncovered through integrative functional genomic approaches. We combine recent analytic advances in heritability estimation and polygenic modeling and leverage recent technological advances in the generation of -omics data to evaluate the nature and scale of the contribution of functional classes of genetic variation to a relatively intractable disorder. We identified cis eQTLs in cerebellum and parietal cortex that capture more than half of the total heritability attributable to SNPs interrogated through GWAS and showed that eQTL-based heritability estimation is highly tissue-dependent. Our findings show that a much greater resolution may be attained than has been reported thus far on the number of common loci that capture a substantial proportion of the heritability to disease risk and that the functional nature of contributory loci may be clarified en masse.

khmer: Working with Big Data in Bioinformatics
Eric McDonald, C. Titus Brown
(Submitted on 9 Mar 2013)

We introduce design and optimization considerations for the ‘khmer’ package.

Comments: Invited chapter for forthcoming book on Performance of Open Source Applications

Comprehensive Detection of Genes Causing a Phenotype using Phenotype Sequencing and Pathway Analysis
Marc Harper, Luisa Gronenberg, James Liao, Christopher Lee
(Submitted on 3 Mar 2013)

Discovering all the genetic causes of a phenotype is an important goal in functional genomics. In this paper we combine an experimental design for multiple independent detections of the genetic causes of a phenotype, with a high-throughput sequencing analysis that maximizes sensitivity for comprehensively identifying them. Testing this approach on a set of 24 mutant strains generated for a metabolic phenotype with many known genetic causes, we show that this pathway-based phenotype sequencing analysis greatly improves sensitivity of detection compared with previous methods, and reveals a wide range of pathways that can cause this phenotype. We demonstrate our approach on a metabolic re-engineering phenotype, the PEP/OAA metabolic node in E. coli, which is crucial to a substantial number of metabolic pathways and under renewed interest for biofuel research. Out of 2157 mutations in these strains, pathway-phenoseq discriminated just five gene groups (12 genes) as statistically significant causes of the phenotype. Experimentally, these five gene groups, and the next two high-scoring pathway-phenoseq groups, either have a clear connection to the PEP metabolite level or offer an alternative path of producing oxaloacetate (OAA), and thus clearly explain the phenotype. These high-scoring gene groups also show strong evidence of positive selection pressure, compared with strictly neutral selection in the rest of the genome.