Peak calling bioinformatics tools | ChIP-seq analysis - OMICtools

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Serves as a transcriptome reconstruction method. Scripture is able to reconstruct a mammalian transcriptome with no prior knowledge of gene annotations. It exploits longer reads that span splice junctions to link discontiguous (spliced) segments. This software identifies short but strongly expressed transcripts, lower transcripts with aggregate evidence and precise gene structures for most of found lincRNA loci.

Allows analysis of ChIP-seq and other functional sequencing data. SPP is an R package providing a data processing pipeline optimized for detection of localized protein binding positions from unpaired sequence reads. This software can examine the saturation level of detected binding position to determine the amount of additional sequencing that may be necessary. It assesses also saturation properties and determines minimal saturated enrichment ratio (MSER).

Identifies peak regions in ChIP-Seq datasets that correspond to sites of transcription factor binding. PeakSeq scores the results of ChIP-Seq experiments by compensating for the mappability map and comparing against a normalized matching control dataset. This method was developed for use with tag sequence data from the Illumina Genome Analyzer platform. It can also be used to identify broader regions of binding that show significant enrichment relative to control.

Detects functional signals in tag profiles from different assays such as histone ChIP-seq, TF ChiP-seq, DNase-seq and FAIRE-seq. DFilter is based on a single receiver operating characteristic – area under the curve (ROC-AUC) optimizing algorithm. This software suits for genomic signals of individual cell types diluted in cellular mixture because the proportion of marginal signals can be mistaken for noise by suboptimal algorithms.

ERANGE / Enhanced Read Analysis of Gene Expression

A software tool for mapping and quantifying Mammalian transcriptomes by RNA-Seq. The functions of ERANGE are to (i) assign reads that map uniquely in the genome to their site of origin and, for reads that match equally well to several sites ('multireads'), assign them to their most likely site(s) of origin; (ii) detect splice-crossing reads and assign them to their gene of origin; (iii) organize reads that cluster together, but do not map to an already known exon, into candidate exons or parts of exons; and (iv) calculate the prevalence of transcripts from each known or newly proposed RNA, based on normalized counts of unique reads, spliced reads and multireads.

HOMER / Hypergeometric Optimization of Motif EnRichment

Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.

MACS / Model-based Analysis for ChIP-Seq

Analyzes data generated by short read sequencers. MACS is a standalone software dedicated to the forecasting of protein-DNA interaction sites from ChIP-Seq. The application is able to: (i) model the distance d and shifting tags by d/2 to enable the spatial resolution of the predicted sites; (ii) capture local biases in the genome by exploiting a dynamic λ local parameter and (iii) evaluate the false discovery rate (FDR) for each detected peak.

Provides a Kernel Density Estimator-based package for analysis of massively parallel sequencing data from chromatin immunoprecipitations. QuEST offers the possibility to search de novo motif on ChIP-seq experiment and to perform gene ontology (GO) analysis on the ChIP-Seq data obtained from the NCBI Short Read Archive (SRA). This software is based on a realistic statistical modeling of the ChIP-Seq.

DANPOS / Dynamic Analysis of Nucleosome and Protein Occupancy by Sequencing

Enables comparative analysis of nucleosome physical organization at single-nucleotide resolution. DANPOS is a bioinformatics pipeline allowing dynamic analysis of nucleosome position and occupancy. The software is useful for detecting functionally relevant dynamic nucleosomes, not only in promoters in yeast, but also in distal regulatory regions with more fuzzy nucleosomes, in complex genomes such as those of mammalian.

SICER / spatial clustering approach for the identification of ChIP-enriched regions

Recognizes ChIP-enriched regions in histone modification data. SCIER is based on a mathematical theory for the score distribution in a genomic background model of random reads. It finds spatial clusters, large and small, unlikely to appear by chance. This tool is able to deal with the enrichment context of a local window in determining its significance. It assists users to reduce the sampling fluctuations in the control library.

Creates a continuous tag sequence density estimation to identify biologically meaningful sites. F-Seq can summarize and display individual sequence data as an accurate and interpretable signal, allowing tag sequencing to identify specific sequence features like regions of open chromatin (DNase-seq) or transcription factor binding sites (ChIP-seq). Outputs results can be visualized through the UCSC Genome Browser.

ZINBA / Zero-Inflated Negative Binomial Algorithm

Recognizes genomic regions enriched for sequenced reads across a wide spectrum of signal patterns and experimental conditions. ZINBA is based on a mixture regression approach that classifies genomic regions into three general components: background, enrichment, and an artificial zero count. It can preprocess data, determine significantly enriched regions, and refine boundary for more narrow sites.

Vancouver Short Read Analysis

A package for collating and searching across thousands of next-generation sequence (NGS) samples. Vancouver Short Read Analysis provides a database can be installed easily to rapidly access and store genetic variation information, compare data from any sequencing platform and perform aggregate analyses. The schema of the database makes rapid and insightful queries simple and enables easy annotation of novel or known genetic variations. Filtering can be done by utilizing annotations, matched pair datasets or datasets marked as non-cancer for separating polymorphisms from putative variants.

Allows to conduct most common analyses of RNAseq and ChIPseq data. SeqBox can be useful for genomic mapping, experimental power evaluation, differential expression, or transcription factors/histone-marks peaks identification between others. This tool is composed of SeqBox software and SeqBox target hardware.

Identifies binding sites, including closely spaced binding sites, from ChIP-seq data. CSDeconv is a computational method that allows calls to be made in the case of closely spaced transcription factor-binding sites. The software was tested using ChIP-seq data for the DosR (dormancy survival regulator) transcription factor in Mycobacterium tuberculosis (MTB) and to existing data collected for the GABP (growth-associated binding protein) transcription factor in humans.

BRACIL / Binding Resolution Amplifier and Cooperative Interaction Locator

Improves binding site resolution and predicts cooperative interactions. BRACIL can study physical properties of DNA shearing from the ChIP-seq coverage. It incorporates motif discovery and is able to detect multiple sites in an enriched region with single nucleotide resolution, high sensitivity, and high specificity. The tool improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate <11% for a set of 47 experimentally validated prokaryotic sites.

A probabilistic method for jointly analyzing multiple ChIP-seq datasets. jMOSAiCS (joint model-based one- and two-sample analysis and inference for ChIP-seq) is a probabilistic model for integrating multiple ChIP-seq datasets to identify combinatorial patterns of enrichment. The key components of jMOSAiCS are base models for the sequencing reads of each individual ChIP-seq experiment and a model that governs the relationship of enrichment among different samples. It facilitates joint analysis of multiple ChIP-seq datasets for both identifying enrichment patterns of a single TF across multiple conditions and characterizing enrichment patterns of multiple epigenomic marks in one or more conditions.

A saturation-based ChIP-seq peak caller. Q uses an assessment of the quadratic enrichment of reads to center candidate peaks followed by statistical analysis of saturation of candidate peaks by 5' ends of reads. This method is not only substantially faster than several competing methods, but also demonstrates statistically significant advantages with respect to reproducibility of results and in its ability to identify peaks with reproducible binding site motifs.

PFMS / Peak-Finder MetaServer

A metaServer application designed for analyzing ChIP-seq data. It integrates several peak-finders within a single interface. A ChIP-seq data file can be analyzed using one or more of the peak-finders (up to 7 among MACS v1.3.7, CisGenome v2.0, Findpeaks v3.1.9.2, HPeak v1.1, E-range v.2.1, SeqSite v1.0 and SISSRs v1.4) and also produces consensus peaks.

GEM / Genome wide Event finding and Motif discovery

Allows investigation of protein-DNA interaction by ChIP-Seq/ChIP-exo data. GEM is based on an integrative computational method and intends to provide both improved spatial accuracy of binding event predictions and improved motif discovery in ChIP-Seq and ChIP-exo datasets. It has been selected to be part of the ENCODE TF ChIP-seq analysis pipeline.

Octopus-toolkit

Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.

Identifies protein–DNA binding events in small bacterial and archaeal genomes. Pique facilitate downstream data visualization, curation and analysis. It also provides a suitable platform for large-scale studies of the structure and dynamic remodeling of gene-regulatory networks. This method is able to operate on systems that have genomic complexities such as IS elements, gene dosage polymorphisms and accessory genomes.

EDD / Enriched Domain Detector

Allows discovery of broad genomic enrichment areas from ChIP-seq data. EDD is a genomic domain caller designed to detect megabase-size domains. The software enables quantitative analysis of ChIP-seq data for proteins widely distributed and with low-level enrichment on chromatin. It can discover genomic domains enriched in lamin A (LMNA) using new ChIP-seq data for LMNA. The main advantages of EDD are its sensitivity to the size of domains rather than the strength of enrichment at a particular site and its robustness against local variations.

Allows identification of transcription factor binding sites (TFBSs) from ChIP-seq data. Peakzilla exploits the bimodal distribution of sequence reads characteristic of true transcription factor (TF) binding events, to identify closely adjacent TFBSs. The software is not meant for the identification of broad enriched regions. It is able to find peaks at high resolution.

Annotates ChIP-seq data analysis. ChIPseeker supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 15,000 bed file information from GEO database. These datasets can be downloaded and compare with user's own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation.

Analyzes ChIP6Seq data and identifies genomic domains marked by diffusive histone modification markers. RSEG doesn’t need control sample to run and can be used to find regions with differential histone modifications patterns with either comparison between two cell types or between two kinds of histone modifications. It can also detect high-quality domain boundaries.

Analyses mapped reads from diverse High-throughput sequencing (HTS) experiments: ChIP-Seq, either punctuated or broad signals, CLIP-Seq and RNA-Seq. Pyicos is a command line utility for the conversion and manipulation of genomic coordinates files. It facilitates HTS analysis through its flexibility and memory efficiency, providing a useful framework for data integration into models of regulatory genomics. Pyicos is part of the Pyicoteo suite of tools.

JAMM / Joint Analysis of NGS replicates via Mixture Model clustering

Assists in finding consensus peaks, determining accurate peak widths and resolving neighboring narrow peaks. JAMM is a peak finder that can integrate information from multiple replicates. It addresses replicate integration by looking at biological replicates as not being exactly reproducible and attempts to model their variability using information about their covariance in local enriched windows. This method is applicable to different types of datasets and can define accurate peak boundaries.

Provides a flexible implementation of the BayesPeak algorithm and is compatible with downstream BioConductor packages. The BayesPeak package introduces a new method for summarizing posterior probability output, along with methods for handling overfitting and support for parallel processing. It provides a Bayesian analysis, with advantages including allowance for overdispersion in read counts and a competitive genome-wide specificity and sensitivity. By anticipating peak structure, BayesPeak does not call peaks based on sheer numbers of reads without appropriate read formation.

Identifies broad peaks in diffuse ChIP-seq datasets. BroadPeak is a distinct approach for calling broad peaks in diffuse ChIP-seq datasets, and able to apply the algorithm to both real and simulated datasets to evaluate its performance. The software appears particularly well suited for calling large peaks as can be seen from the more contiguous characterization of known large peaks from the simulated datasets and the relatively broader distribution of called peak sizes.

PePr / Peak Prioritization pipeline

A ChIP-seq peak-calling and prioritization pipeline that uses a sliding window approach and models read counts across replicates and between groups with a negative binomial distribution. PePr empirically estimates the optimal shift/fragment size and sliding window width, and estimates dispersion from the local genomic area. Regions with less variability across replicates are ranked more favorably than regions with greater variability. Optional post-processing steps are also made available to filter out peaks not exhibiting the expected shift size and/or to narrow the width of peaks.

HMCan / Histone Modification in Cancer

Identifies histone modifications in genomes with large copy number alterations. HMCan is a tool for detection of histone modifications in cancer samples using ChIP-seq data. The software corrects for copy number bias and for GC-content bias and then uses hidden markov models (HMMs) to detect regions rich in histone modifications. It can also calculate posterior probabilities for each bin. HMCan was applied on both simulated and experimental data.

A general methodological framework to rigorously combine the evidence of enriched regions in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. Given a set of peaks from (biological or technical) replicates, the method combines the p-values of overlapping enriched regions: users can choose a threshold on the combined significance of overlapping peaks and set a minimum number of replicates where the overlapping peaks should be present. The method allows the "rescue" of weak peaks occuring in more than one replicate and outputs a new set of enriched regions for each replicate.

Pasha / Preprocessing of Aligned Sequences from HTS Analyses

An R package designed for processing aligned reads from chromatin-oriented high-throughput sequencing experiments. Pasha allows easy manipulation of aligned reads from short-read sequencing technologies (ChIP-seq, FAIRE-seq, MNase-seq...) and offers innovative approaches such as ChIP-seq reads elongation, nucleosome midpoint piling strategy for positioning analyses, or the ability to subset paired-end reads by groups of insert size that can contain biologically relevant information. It integrates several options allowing a seamless adaptation to various experimental setups. Additionally, the R package provides several tools for programmers that need to develop or integrate additional features.

Allows users to discretize ChIP-seq data. Zerone combines an arbitrary number of ChIP-seq replicates in a single discretized profile, where conflicts are resolved by maximizing the likelihood of the underlying statistical model. This program controls the quality of its output to detect potential anomalies. This tool produces congruent window-based outputs, and it can process hundreds of experiments per day on average hardware.

Calls peaks in ChIP-seq data with biological replicates. BinQuasi jointly models biological replicates by employing a generalized linear model framework. This program identifies peaks by using a one-sided quasi-likelihood ratio test in quasi-Poisson or quasi-negative binomial models. It can control false discovery rate (FDR) at the window-level and take into account that the windows may be correlated.

Computes the annotation error of peak calls. PeakError allows, after constructing a database of annotated regions that represent your visual interpretation of the peak locations in a ChIP-seq experiment, to compute the error of a peak calling model. PeakError proposes to create labels that encode an experienced scientist’s judgment about which regions contain or do not contain peaks. The labels can then be used as a gold standard to quantitatively train and test peak detection algorithms on specific data sets.

Allows transcription factor binding site (TFBS) identification. dPeak is a high-resolution binding site identification (deconvolution) algorithm that can utilize both paired-end tag (PET) and single-end tag (SET) ChIP-Seq data. The software implements a probabilistic model that describes the ChIP-Seq data generation process and analytically quantifies the differences in resolution between the PET and SET ChIP-Seq assays.

NEXT-peak / normal-exponential two-peak

Identifies binding sites from ChIP-seq data. NEXT-peak is a rigorous statistical model that calls peaks from ChIP-seq data for transcription factor binding sites. It mimics the underlying data-generating process in ChIP-seq experiments by using the dual density of a normal-exponential two-peak model. The software stands alone in quantifying errors by reporting a standard error for its estimates of binding intensity.

Locates signal peaks in ChIP-Seq data targeted at transcription factors. ChIP-Peak is a Narrow peak caller using a fixed width peak size. The software allows detection of regions (peaks) in the genome that are enriched in ChIP-Seq tags.

An efficient and effective method to predict constitutive protein binding sites using ChIP-seq peaks from multiple cell lines. Besides constitutive binding sites for a given protein, T-KDE can identify genomic "hot spots" where several different proteins bind and, conversely, cell-type-specific sites bound by a given protein.

CSAR / ChIP-Seq Analysis in R

An R package for the statistical analysis of ChIP-seq experiments. CSAR calculates single-nucleotide read-enrichment values, taking the average size of DNA fragments subjected to sequencing into account. After normalization, sample and control are compared using a test based on the ratio test or the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutations. Computational efficiency is achieved by implementing the most time-consuming functions in C++ and integrating these in the R package.

An R package for ranking ChIP-seq peaks with shape information. polyaPeak uses a Bayesian hierarchical model to incorporate the peak shapes in the ranking. The shapes are described by a mixture of multivariate Polya distributions. Real data analyses show that polyaPeak outperforms several popular peak calling software including MACS, CisGenome and PICS.

Calls ChIP-seq peaks. OccuPeak is a standalone software able to model the background by exploiting abundant low frequency tags present in each ChIP-seq dataset. The application generates files, compatible with the UCSC genome browser, that summarizes information on the reconstructed fragment length and, optionally, that compiles model parameters, genome coverage and number of peaks collected for each sampling window or chromosome.

BiSA / Binding Sites Analyser

Identifies genes located near binding regions of interest, genomic features near a gene or locus of interest and statistical significance of overlapping regions can also be reported. BiSA is a transcription factor DNA binding site analyser software for archiving of binding regions and easy identification of overlap with or proximity to other regions of interest. It is capable of reporting overlapping regions that share common base pairs; regions that are nearby; regions that are not overlapping; and average region sizes.

Analyzes ChIPSeq data for identifying protein-interacting genomic regions. HPeak is a hidden Markov model-based approach and is thus compatible with rigorous statistical inference. The software can be useful to researchers conducting ChIP-Seq experiments. It was utilized in study to map the genomic landscape of master transcriptional regulators of prostate cancer.

DROMPA / DRaw and Observe Multiple enrichment Profiles and Annotation

Allows peak calling, visualization, quality check and Polymerase Chain Reaction (PCR) bias filtering of ChIP-seq data. DROMPA calls peaks by comparing the read distribution of the ChIP sample with that of the corresponding input sample. The software identifies peaks as bar graph protein-binding sites when the peaks are sharp (approximately 1 kbp) and when they are broad (approximately 1 Mbp). It can accept multiple mapped reads (reads mapped on multiple loci of the reference genome).

A peak-caller package that works equally well on punctate and broad sites. PeakRanger can resolve closely-spaced peaks, has excellent performance, and is easily customized. It can be run in a parallel cloud computing environment to obtain extremely high performance on very large data sets. PeakRanger has above-average spatial accuracy in terms of identifying the precise location of binding events. PeakRanger also has excellent sensitivity and specificity in all benchmarks evaluated.

SIPeS / Site Identification from Paired-end Sequencing

Calculates effective genome size and identifies binding sites from shirt reads generated from paired-end solexa ChIP-Seq technology. SIPeS can detect peak end and start positions based on a dynamic baseline and discriminate closely adjacent binding sites to easily separate adjacent overlapping peaks. This software can also measure broad peaks with high signal levels while a peak of lower signal value has every local maximum.

COPAR / ChIP-seq Optimal Peak AnalyzeR

Measures Next-generation sequencing (NGS)/ChIP-seq experiment quality through global peak alignment comparison. COPAR can extract genomic features based on spectrum method for in-depth analysis of ChIPsequencing profiles. It is able to process mapped read file in BED format and output statistically sound results for diverse high-throughput sequencing (HTS) experiments.

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1 - 17 of 17 results

1 - 17 of 17 results

COV2HTML DANPOS F-Seq

Bioinformatician omicX

PhD ès Neurosciences, I worked 8 years on the brain and its diseases. I then specialized in bioinformatics (NGS, epigenetics) and worked in CEA and GENETHON before to join OMICX and help OMICtools community.

aCNViewer MEDOC ngs.plot

Group leader in Computational RNA Biology Pompeu Fabra University

Pyicos MIRA SUPPA

Big data scientist National institute of Plant Gneome Reseaech (NIPGR)

Research Interests Gene fusion detection in Plants Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions are well known in case of human. But, in plants, this phenomenon is not yet explored. We are planning to discover the fusion transcripts/gene fusions in different type of plants by using RNA-Seq datasets. Further, we are planning to understand the mechanism of gene fusion formation and significance of fusions in plants. Whole genome and transcriptome sequencing data analysis of Plants In this era of Next Generation Sequencing (NGS), there is huge amount of sequencing data available in the public domain. Any novel finding from these available datasets is major challenge for a computational biologist. We are interested in the analysis of whole genome and transcriptome sequencing data of different plants to fetch out the useful information from those datasets, with the help of bioinformatics tools. Currently, we are planning to study the gene clusters of secondary metabolite pathways in different plants. Development of webservers, databases and computational pipelines for plant research Development of database is necessary to compile and share the information with scientific community. We are dedicated to develop useful databases and webserver for plant research. Another area of interest is to develop automated pipelines and tools for the analysis of high throughput genomics data, generated by NGS technologies. Professional & Academic Background Staff Scientist II (May 2017- present): National Institute of Plant Genome Research (NIPGR), New Delhi, India Postdoctoral Research Associate (2015-2017): University Of Virginia, Charlottesville, VA, USA Research Scientist (2014-2015): Sir Ganga Ram Hospital, New Delhi, India PhD Bioinformatics (2009-2014): Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh under Jawaharlal Nehru University (JNU), New Delhi, India M.Sc. Life Sciences (2007-2009): Jawaharlal Nehru University (JNU), New Delhi, India B.Sc. Biotechnology (2004-2007): Jamia Millia Islamia (JMI), New Delhi, India Awards and Fellowships Junior and Senior Research Fellowship (2009-2014): Council of Scientific and Industrial Research (CSIR), New Delhi, India GATE (Graduate Aptitude Test in Engineering): Qualified in years 2008 and 2009 Scientific Contributions/ Recognitions Associate editor: Journal of Translational Medicine. Editorial Board Member of Journal: Theoretical Biology and Medical Modelling. Reviewer: PloS One, BMC Genomics, BMC Bioinformatics, BMC Biology, BMC Biotechnology, Frontiers in Physiology and several other journals. Web Resources/ Databases (Developed/ Contributed) A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer (http://www.imtech.res.in/raghava/cancertope/) GenomeABC: A webserver for benchmarking of genome assemblers. (http://crdd.osdd.net/raghava/genomeabc/). Genomics web portal page. (http://crdd.osdd.net/raghava/genomesrs/). Map/Alignment module of CancerDr: Cancer Drug Resistance Database. (http://crdd.osdd.net/raghava/cancerdr/). Short reads and contigs alignment module of PCMDB: Pancreatic cancer methylation database. (http://crdd.osdd.net/raghava/pcmdb/). Burkholderia sp. SJ98 database. (http://crdd.osdd.net/raghava/genomesrs/burkholderia/). Rhodococcus imtechensis RKJ300 database. (http://crdd.osdd.net/raghava/genomesrs/rkj300/). Genotrick: A pipeline for whole genome assembly and annotation of Genomes (http://crdd.osdd.net/raghava/genomesrs/genotrick/) Development of Debian packages in OSDDlinux: A Customized Operating System for Drug Discovery. (http://osddlinux.osdd.net/). A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. (http://crdd.osdd.net/raghava/mtbveb/).

FusionFinder comrad

Mahantesh Biradar

‎Graduate PhD researcher focusing on Cardiovascular Epidemiology & Social Media Enthusiast Research Stash

I'm a Graduate PhD researcher in the area of Biomedical Science and focussing on the Pharmacogenomics/Pharmacogenomics of Diabetes and related complications

Sangram keshari sahu

Computational Biology and Bioinformatics Indian Institute of Science Education and Research (IISER)

MACS Bowtie BWA

Bioinformatics Scientist --

nucChIP DANPOS iNuc-PseKNC

epitranscriptomics Dundee, UK

RATs DFilter histoneHMM

Research Associate CNRS

Peakzilla

I am a student. UNIVERSITI SAINS MALAYSIA

JAMM ChIPseeker Aging Chart

olga Adame Guerero

Student Universidad Autonoma de Guerrero

Epigenetics researcher Diagenode SA

MACS Bismark SICER

Brennan Gerlach

PhD Candidate in Molecular Cellular Physiology Albany Medical College

NGS-FC Bowtie HOMER

Postdoc Oregon Health and Science University

MACS

Bioinformatician in training University of Guelph

DFilter

Bioinfomagician Dankook university

Octopus-toolkit

I am an Electrical Engineer and Developer University of Engineering and Technology

dDocent VarScan GATK

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