Biology subjects

Bafna, V.

Publications and source records attributed to Bafna, V..

44 records · Page 3Linked to original sources

bioRxiv2019

A Note on Computing Interval Overlap Statistics

We consider the following problem: Let I and If each describe a collection of n and m non-overlapping intervals on a line segment of finite length. Suppose that k of the m intervals of If are intersected by some interval(s) in I. Under the null hypothesis that intervals in I are randomly arranged w.r.t If, what is the significance of this overlap? This is a natural abstraction of statistical questions that are ubiquitous in the post-genomic era. The interval collections represent annotations that reveal structural or functional regions of the genome, and overlap statistics can provide insight into the correlation between different structural and functional regions. However, the statistics of interval overlaps have not been systematically explored. In this manuscript, we formulate a statistical significance problem which considers the length and structure of intervals. We describe a combinatorial algorithm for a constrained interval overlap problem that can accurately compute very small p-values. We also propose a fast approximate method to facilitate problems consisted of very large number of intervals. These methods are all implemented in a tool, iStat. We applied iStat to simulated interval data to obtain precise estimates of low p-values, and characterize the performance of our methods. We also test iStat on real datasets from previous studies, and compare iStat results with the reported p-values using basic permutation or parametric tests. The iStat software is made publicly available on https://github.com/shahab-sarmashghi/ISTAT.git

genomics↗

bioRxiv2018

Proteogenomic annotation of the Chinese hamster reveals extensive novel translation events and endogenous retroviral elements

A high quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrectly predictions of translated sequence, incorrect splice isoforms and missing genes. Here we generated a draft annotation for the newly assembled Chinese hamster genome and used RNA-Seq, proteomics, and Ribo-Seq to experimentally annotate the genome. We identified 4,333 new proteins compared to the hamster RefSeq protein annotation and 2,503 novel translational events (e.g., alternative splices, mutations, novel splices). Finally, we used this pipeline to identify the source of translated retroviruses contaminating recombinant products from Chinese hamster ovary (CHO) cell lines, including 131 type-C retroviruses, thus enabling future efforts to eliminate retroviruses by reducing the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate platform for guiding CHO cell line engineering, including facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

bioinformatics↗

bioRxiv2018

Reconstructing and characterizing focal amplifications in cancer using AmpliconArchitect

Focal oncogene amplification and rearrangements drive tumor growth and evolution in multiple cancer types. We developed a tool, AmpliconArchitect (AA), which can robustly reconstruct the fine structure of focally amplified regions using whole genome sequencing. AA-reconstructed amplicons in pan-cancer data and in virus-driven cervical cancer samples revealed many novel insights about focal amplifications. Specifically, the findings lend support to extrachromosomally mediated mechanisms for copy number expansion, and oncoviral pathogenesis.

bioinformatics↗

bioRxiv2017

Assembly-free and alignment-free sample identification using genome skims

The ability to quickly and inexpensively describe taxonomic diversity is critical in this era of rapid climate and biodiversity changes. The currently preferred molecular technique, barcoding, has been very successful, but is based on short organelle markers. Recently, an alternative genome-skimming approach has been proposed: low-pass sequencing (100Mb - several Gb per sample) is applied to voucher and/or query samples, and marker genes and/or organelle genomes are recovered computationally. The current practice of genome-skimming discards the vast majority of the data because the low coverage of genome-skims prevents assembling the nuclear genomes. In contrast, we suggest using all unassembled reads directly, but existing methods poorly support this goal. We introduce a new alignment-free tool, Skmer, to estimate genomic distances between the query and each reference genome-skim using the k-mer decomposition of reads. We test Skmer on a large set of insect and bird genomes, sub-sampled to create genome-skims. Skmer shows great accuracy in estimating genomic distances, identifying the closest match in a reference dataset, and inferring the phylogeny. The software is publicly available on https://github.com/shahab-sarmashghi/Skmer.git

ecology↗

bioRxiv2017

Targeted Genotyping of Variable Number Tandem Repeats with adVNTR

Whole Genome Sequencing is increasingly used to identify Mendelian variants in clinical pipelines. These pipelines focus on single nucleotide variants (SNVs) and also structural variants, while ignoring more complex repeat sequence variants. We consider the problem of genotyping Variable Number Tandem Repeats (VNTRs), composed of inexact tandem duplications of short (6-100bp) repeating units. VNTRs span 3% of the human genome, are frequently present in coding regions, and have been implicated in multiple Mendelian disorders. While existing tools recognize VNTR carrying sequence, genotyping VNTRs (determining repeat unit count and sequence variation) from whole genome sequenced reads remains challenging. We describe a method, adVNTR, that uses Hidden Markov Models to model each VNTR, count repeat units, and detect sequence variation. adVNTR models can be developed for short-read (Illumina) and single molecule (PacBio) whole genome and exome sequencing, and show good results on multiple simulated and real data sets. adVNTR is available at https://github.com/mehrdadbakhtiari/adVNTR

bioinformatics↗

bioRxiv2017

The antibody repertoire of colorectal cancer

3Immunotherapy is becoming increasingly important in the fight against cancers, utilizing and manipulating the bodys immune response to treat tumors. Understanding the immune repertoire - the collection of immunological proteins - of treated and untreated cells is possible at the genomic, but technically difficult at the protein level. Standard protein databases do not include the highly divergent sequences of somatic rearranged immunoglobulin genes, and may lead to missed identifications in a mass spectrometry search. We introduce a novel proteogenomic approach, AbScan, to identify these highly variable antibody peptides, by developing a customized antibody database construction method using RNA-seq reads aligned to immunoglobulin (Ig) genes.\n\nAbScan starts by filtering transcript (RNA-seq) reads that match the template for Ig genes. The retained reads are used to construct a repertoire graph using the split de Bruijn graph: a graph structure that improves upon the standard de Bruijn graph to capture the high diversity of Ig genes in a compact manner. AbScan corrects for sequencing errors, and converts the graph to a format suitable for searching with MS/MS search tools. We used AbScan to create an antibody database from 90 RNA-seq colorectal tumor samples. Next, we used proteogenomics analysis to search MS/MS spectra of matched colorectal samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) against the AbScan generated database. AbScan identified 1, 940 distinct antibody peptides. Correlating with previously identified Single Amino-Acid Variants (SAAVs) in the tumor samples, we identified 163 pairs (antibody peptide, SAAV) with significant co-occurrence pattern in the 90 samples. The presence of co-expressed antibody and mutated peptides was correlated with survival time of the individuals. Our results suggest that AbScan(https://github.com/csw407/AbScan.git) is an effective tool for a proteomic exploration of the immune response in cancers.\n\n\n\nO_TBL View this table:\norg.highwire.dtl.DTLVardef@62001corg.highwire.dtl.DTLVardef@1f542faorg.highwire.dtl.DTLVardef@10048d0org.highwire.dtl.DTLVardef@1ac5a75org.highwire.dtl.DTLVardef@66db0f_HPS_FORMAT_FIGEXP M_TBL C_TBL

bioinformatics↗

bioRxiv2017

Fine-mapping the Favored Mutation in a Positive Selective Sweep

Methods to identify signatures of selective sweeps in population genomics data have been actively developed, but mostly do not identify the specific mutation favored by the selective sweep. We present a method, iSAFE, that uses a statistic derived solely from population genetics signals to pinpoint the favored mutation even when the signature of selection extends to 5Mbp. iSAFE was tested extensively on simulated data and in human populations from the 1000 Genomes Project, at 22 loci with previously characterized selective sweeps. For 14 of the 22 loci, iSAFE ranked the previously characterized candidate mutation among the 13 highest scoring (out of [~] 21, 000 variants). Three loci did not show a strong signal. For the remaining loci, iSAFE identified previously unreported mutations as being favored. In these regions, all of which involve pigmentation related genes, iSAFE identified identical selected mutations in multiple non-African populations suggesting an out-of-Africa onset of selection. The iSAFE software can be downloaded from https://github.com/alek0991/iSAFE.

evolutionary biology↗

bioRxiv2017

Ultra-Accurate Genome Sequencing And Haplotyping Of Single Human Cells

Accurate detection of variants and long-range haplotypes in genomes of single human cells remains very challenging. Common approaches require extensive in vitro amplification of genomes of individual cells using DNA polymerases and high-throughput short-read DNA sequencing. These approaches have two notable drawbacks. First, polymerase replication errors could generate tens of thousands of false positive calls per genome. Second, relatively short sequence reads contain little to no haplotype information. Here we report a method, which is dubbed SISSOR (Single-Stranded Sequencing using micrOfluidic Reactors), for accurate single-cell genome sequencing and haplotyping. A microfluidic processor is used to separate the Watson and Crick strands of the double-stranded chromosomal DNA in a single cell and to randomly partition megabase-size DNA strands into multiple nanoliter compartments for amplification and construction of barcoded libraries for sequencing. The separation and partitioning of large single-stranded DNA fragments of the homologous chromosome pairs allows for the independent sequencing of each of the complementary and homologous strands. This enables the assembly of long haplotypes and reduction of sequence errors by using the redundant sequence information and haplotype-based error removal. We demonstrated the ability to sequence single-cell genomes with error rates as low as 10-8 and average 500kb long DNA fragments that can be assembled into haplotype contigs with N50 greater than 7Mb. The performance could be further improved with more uniform amplification and more accurate sequence alignment. The ability to obtain accurate genome sequences and haplotype information from single cells will enable applications of genome sequencing for diverse clinical needs.

genomics↗