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Binay Panda

Publications and source records attributed to Binay Panda.

2 recordsLinked to original sources
arXiv2013

SInC: An accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data

We report SInC (SNV, Indel and CNV) simulator and read generator, an open-source tool capable of simulating biological variants taking into account a platform-specific error model. SInC is capable of simulating and generating single- and paired-end reads with user-defined insert size with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes. Sinc can be downloaded from https://sourceforge.net/projects/sincsimulator/.

q-bio.QM↗
arXiv2013

Augmenting transcriptome assembly combinatorially

RNA-seq allows detection and precise quantification of transcripts, provides comprehensive understanding of exon/intron boundaries, aids discovery of alternatively spliced isoforms and fusion transcripts along with measurement of allele-specific expression. Researchers interested in studying and constructing transcriptomes, especially for non-model species, often face the conundrum of choosing from a number of available de novo and genome-guided assemblers. A comprehensive comparative study is required to assess and evaluate their efficiency and sensitivity for transcript assembly, reconstruction and recovery. None of the popular assembly tools in use today achieves requisite sensitivity, specificity or recovery of full-length transcripts on its own. Hence, it is imperative that methods be developed in order to augment assemblies generated from multiple tools, with minimal compounding of error. Here, we present an approach to combinatorially augment transciptome assembly based on a rigorous comparative study of popular de novo and genome-guided transcriptome assembly tools.

q-bio.GN↗