Biology subjects

Ivan Iossifov

Publications and source records attributed to Ivan Iossifov.

5 recordsLinked to original sources

bioRxiv2016

MUMdex: MUM-based structural variation detection

MotivationStandard genome sequence alignment tools primarily designed to find one alignment per read have difficulty detecting inversion, translocation and large insertion and deletion (indel) events. Moreover, dedicated split read alignment methods that depend only upon the reference genome may misidentify or find too many potential split read alignments because of reference genome anomalies.\n\nMethodsWe introduce MUMdex, a Maximal Unique Match (MUM)-based genomic analysis software package consisting of a sequence aligner to the reference genome, a storage-indexing format and analysis software. Discordant reference alignments of MUMs are especially suitable for identifying inversion, translocation and large indel differences in unique regions. Extracted population databases are used as filters for flaws in the reference genome. We describe the concepts underlying MUM-based analysis, the software implementation and its usage.\n\nResultsWe demonstrate via simulation that the MUMdex aligner and alignment format are able to correctly detect and record genomic events. We characterize alignment performance and output file sizes for human whole genome data and compare to Bowtie 2 and the BAM format. Preliminary results demonstrate the practicality of the analysis approach by detecting de novo mutation candidates in human whole genome DNA sequence data from 510 families. We provide a population database of events from these families for use by others.\n\nAvailabilityhttp://mumdex.com/\n\nContactandrewsp@cshl.edu (or paa@drpa.us)\n\nSupplementary informationSupplementary data are available online.

Genomics↗

bioRxiv2015

Indel variant analysis of short-read sequencing data with Scalpel

As the second most common type of variations in the human genome, insertions and deletions (indels) have been linked to many diseases, but indels of more than a few bases are still challenging to discover from short-read sequencing data. Scalpel (http://scalpel.sourceforge.net) is open-source software for reliable indel detection based on the micro-assembly technique. To date, it has been successfully used to discover mutations in novel candidate genes for autism, and is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole genome and exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation for single sample and somatic analysis. Indel normalization, visualization, and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be finished in ~6 hours after read mapping.

Bioinformatics↗

bioRxiv2015

Genome Wide Variant Analysis of Simplex Autism Families with an Integrative Clinical-Bioinformatics Pipeline

Autism spectrum disorders (ASD) are a group of developmental disabilities that affect social interaction, communication and are characterized by repetitive behaviors. There is now a large body of evidence that suggests a complex role of genetics in ASD, in which many different loci are involved. Although many current population scale genomic studies have been demonstrably fruitful, these studies generally focus on analyzing a limited part of the genome or use a limited set of bioinformatics tools. These limitations preclude the analysis of genome-wide perturbations that may contribute to the development and severity of ASD-related phenotypes. To overcome these limitations, we have developed and utilized an integrative clinical and bioinformatics pipeline for generating a more complete and reliable set of genomic variants for downstream analyses. Our study focuses on the analysis of three simplex autism families consisting of one affected child, unaffected parents, and one unaffected sibling. All members were clinically evaluated and widely phenotyped. Genotyping arrays and whole genome sequencing were performed on each member, and the resulting sequencing data were analyzed using a variety of available bioinformatics tools. We searched for rare variants of putative functional impact that were found to be segregating according to de-novo, autosomal recessive, x-linked, mitochondrial and compound heterozygote transmission models. The resulting candidate variants included three small heterozygous CNVs, a rare heterozygous de novo nonsense mutation in MYBBP1A located within exon 1, and a novel de novo missense variant in LAMB3. Our work demonstrates how more comprehensive analyses that include rich clinical data and whole genome sequencing data can generate reliable results for use in downstream investigations. We are moving to implement our framework for the analysis and study of larger cohorts of families, where statistical rigor can accompany genetic findings.

Genetics↗

bioRxiv2014

Reducing INDEL calling errors in whole-genome and exome sequencing data

BackgroundINDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.\n\nMethodsWe characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low quality INDELs (7% vs. 51%).\n\nResultsSimulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (52%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (85% vs. 54%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.\n\nConclusionsOverall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (e.g. capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Bioinformatics↗

bioRxiv2013

Accurate detection of de novo and transmitted INDELs within exome-capture data using micro-assembly

We present a new open-source algorithm, Scalpel, for sensitive and specific discovery of INDELs in exome-capture data. By combining the power of mapping and assembly, Scalpel searches the de Bruijn graph for sequence paths (contigs) that span each exon. The algorithm creates a single path for exons with no INDEL, two paths for an exon with a heterozygous mutation, and multiple paths for more exotic variations. A detailed repeat composition analysis coupled with a self-tuning k-mer strategy allows Scalpel to outperform other state-of-the-art approaches for INDEL discovery. We extensively compared Scalpel with a battery of >10000 simulated and >1000 experimentally validated INDELs between 1 and 100bp against two recent algorithms for INDEL discovery: GATK HaplotypeCaller and SOAPindel. We report anomalies for these tools in their ability to detect INDELs, especially in regions containing near-perfect repeats which contribute to high false positive rates. In contrast, Scalpel demonstrates superior specificity while maintaining high sensitivity. We also present a large-scale application of Scalpel for detecting de novo and transmitted INDELs in 593 families with autistic children from the Simons Simplex Collection. Scalpel demonstrates enhanced power to detect long ([≥]20bp) transmitted events, and strengthens previous reports of enrichment for de novo likely gene-disrupting INDEL mutations in children with autism with many new candidate genes. The source code and documentation for the algorithm is available at http://scalpel.sourceforge.net.

Bioinformatics↗