IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures

Motivation: Recent advance in next generation sequencing has triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to exploring robust signatures for clinical translation is increasingly highlighted, attributed to the clinical success of immune checkpoint blockade in diverse malignancies. However, effective tools to comprehensively interpret multi-omics data is still warranted to provide increased granularity into intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Results: We developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing comprehensive investigation of estimation of reported or user-built signatures, TME deconvolution and signature construction base on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, lncRNA profiling, genomic characteristics and signatures generated from single-cell RNA sequencing data in different cancer settings. Additionally, IOBR also integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool, to leverage multi-omics data to facilitate immuno-oncology exploration and unveiling of tumor-immune interactions and accelerating precision immunotherapy.

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PIMBA: a PIpeline for MetaBarcoding Analysis

10.1101/2021.03.23.436646 ◽

2021 ◽

Author(s):

Renato R. M. Oliveira ◽

Raissa L S Silva ◽

Gisele L. Nunes ◽

Guilherme Oliveira

Keyword(s):

Environmental Samples ◽

Next Generation Sequencing Data ◽

Ecological Studies ◽

Sequencing Data ◽

Monitoring Method ◽

Computational Tools ◽

Dna Metabarcoding ◽

Reference Databases ◽

User Friendly ◽

Generation Sequencing

DNA metabarcoding is an emerging monitoring method capable of assessing biodiversity from environmental samples (eDNA). Advances in computational tools have been required due to the increase of Next-Generation Sequencing data. Tools for DNA metabarcoding analysis, such as MOTHUR, QIIME, Obitools, and mBRAVE have been widely used in ecological studies. However, some difficulties are encountered when there is a need to use custom databases. Here we present PIMBA, a PIpeline for MetaBarcoding Analysis, which allows the use of customized databases, as well as other reference databases used by the softwares mentioned here. PIMBA is an open-source and user-friendly pipeline that consolidates all analyses in just three command lines.

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Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1808403116 ◽

2018 ◽

Vol 116 (3) ◽

pp. 950-959 ◽

Cited By ~ 16

Author(s):

Patrick Maffucci ◽

Benedetta Bigio ◽

Franck Rapaport ◽

Aurélie Cobat ◽

Alessandro Borghesi ◽

...

Keyword(s):

Reference Genome ◽

Low Complexity ◽

Next Generation Sequencing Data ◽

Sequencing Data ◽

Human Patient ◽

Reference Genome Assembly ◽

Exome Analysis ◽

User Friendly ◽

Computational Analyses ◽

Generation Sequencing

Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants (NPVs) as possible, without removing the true disease-causing mutations. This involves comparing the patient’s exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance. However, variants frequent in a given exome cohort, but absent or rare in public databases, have also been reported and treated as NPVs, without rigorous exploration. We report the generation of a blacklist of variants frequent within an in-house cohort of 3,104 exomes. This blacklist did not remove known pathogenic mutations from the exomes of 129 patients and decreased the number of NPVs remaining in the 3,104 individual exomes by a median of 62%. We validated this approach by testing three other independent cohorts of 400, 902, and 3,869 exomes. The blacklist generated from any given cohort removed a substantial proportion of NPVs (11–65%). We analyzed the blacklisted variants computationally and experimentally. Most of the blacklisted variants corresponded to false signals generated by incomplete reference genome assembly, location in low-complexity regions, bioinformatic misprocessing, or limitations inherent to cohort-specific private alleles (e.g., due to sequencing kits, and genetic ancestries). Finally, we provide our precalculated blacklists, together with ReFiNE, a program for generating customized blacklists from any medium-sized or large in-house cohort of exome (or other next-generation sequencing) data via a user-friendly public web server. This work demonstrates the power of extracting variant blacklists from private databases as a specific in-house but broadly applicable tool for optimizing exome analysis.

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iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

10.1101/2021.09.18.460896 ◽

2021 ◽

Author(s):

Anjana Anilkumar Sithara ◽

Devi Priyanka Maripuri ◽

Keerthika Moorthy ◽

Sai Sruthi Amirtha Ganesh ◽

Philge Philip ◽

...

Keyword(s):

Data Analysis ◽

Workflow Management ◽

Human Monocyte ◽

Complex Data ◽

Omics Data ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Sequencing Technologies ◽

Fastq Format ◽

User Friendly

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics on the nature of the data. Our iCOMIC toolkit pipeline can analyze whole-genome and transcriptome data and is embedded in the popular Snakemake workflow management system. iCOMIC is characterized by a user-friendly GUI that offers several advantages, including executing analyses with minimal steps, eliminating the need for complex command-line arguments. The toolkit features many independent core workflows for both whole genomic and transcriptomic data analysis. Even though all the necessary, well-established tools are integrated into the pipeline to enable "out-of-the-box" analysis, we provide the user with the means to replace modules or alter the pipeline as needed. Notably, we have integrated algorithms developed in-house for predicting driver and passenger mutations based on mutational context and tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle (GIAB) benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM - GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r=0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, with minimal steps, significantly ameliorating complex data analysis pipelines. Availability: https://github.com/RamanLab/iCOMIC

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SAVE: A secure cloud-based pipeline for CRISPR pooled screen deconvolution

10.1101/110262 ◽

2017 ◽

Cited By ~ 2

Author(s):

Hyun-Hwan Jeong ◽

Seon Young Kim ◽

Maxime WC Rosseaux ◽

Huda Y Zoghbi ◽

Zhandong Liu

Keyword(s):

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Analysis Pipeline ◽

Web Based ◽

Screening Experiments ◽

Accelerate Development ◽

User Friendly ◽

Generation Sequencing

AbstractWe present a user-friendly, cloud-based, data analysis pipeline for the deconvolution of pooled screening data. This tool, termed SAVE for Screening Analysis Visual Explorer, serves a dual purpose of extracting, clustering and analyzing raw next generation sequencing files derived from pooled screening experiments while at the same time presenting them in a user-friendly way on a secure web-based platform. Moreover, SAVE serves as a useful web-based analysis pipeline for reanalysis of pooled CRISPR screening datasets. Taken together, the framework described in this study is expected to accelerate development of web-based bioinformatics tool for handling all studies which include next generation sequencing data. SAVE is available at http://save.nrihub.org.

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OmicsX: a web server for integrated OMICS analysis

10.1101/755918 ◽

2019 ◽

Author(s):

Jianbo Pan ◽

Jiang Qian ◽

Hui Zhang

Keyword(s):

Web Server ◽

Biological Molecules ◽

Data Sets ◽

Omics Data ◽

Annotation Information ◽

Integrated Omics ◽

User Friendly ◽

Deep Exploration ◽

Modified Forms ◽

Omic Data

ABSTRACTHigh-throughput omic data sets like genomics, transcriptomics, proteomics, glycomics, lipitomics, metabolomics, and modified forms of the biological molecules have been generated to investigate biological mechanisms. Accordingly, bioinformatic methods and tools have been developed and applied to analyze the omics data for different purposes. However, a lack of comparison and integration tools impedes the deep exploration of multi-omics data. OmicsX is a user-friendly web server for integration and comparison of different omic datasets with optional sample annotation information. The tool includes modules for gene-wise correlation, sample-wise correlation, subtype clustering, and differential expression. OmicsX provides researchers the analysis results with visualization, searching, and data downloading, and help to suggest the biological indications from the comparison of different omic data.Availability and implementationhttp://bioinfo.wilmer.jhu.edu/OmicsX/

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SMaSH: Sample matching using SNPs in humans

BMC Genomics ◽

10.1186/s12864-019-6332-7 ◽

2019 ◽

Vol 20 (S12) ◽

Cited By ~ 2

Author(s):

Maximillian Westphal ◽

David Frankhouser ◽

Carmine Sonzone ◽

Peter G. Shields ◽

Pearlly Yan ◽

...

Keyword(s):

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Data Sets ◽

Omics Data ◽

Rna Seq ◽

Next Generation ◽

Sequencing Data ◽

Data Types ◽

Two Samples ◽

Generation Sequencing

Abstract Background Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not. Methods We select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets. Results We validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification. Conclusion Our tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data.

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Ultraplex: A rapid, flexible, all-in-one fastq demultiplexer

Wellcome Open Research ◽

10.12688/wellcomeopenres.16791.1 ◽

2021 ◽

Vol 6 ◽

pp. 141

Author(s):

Oscar G Wilkins ◽

Charlotte Capitanchik ◽

Nicholas M. Luscombe ◽

Jernej Ule

Keyword(s):

Ribosome Profiling ◽

Sequencing Analysis ◽

Sequencing Data ◽

Illumina Hiseq ◽

Pcr Duplicates ◽

Next Generation Sequencing Analysis ◽

Computationally Intensive ◽

User Friendly ◽

Paired End Sequencing ◽

Generation Sequencing

Background: The first step of virtually all next generation sequencing analysis involves the splitting of the raw sequencing data into separate files using sample-specific barcodes, a process known as “demultiplexing”. However, we found that existing software for this purpose was either too inflexible or too computationally intensive for fast, streamlined processing of raw, single end fastq files containing combinatorial barcodes. Results: Here, we introduce a fast and uniquely flexible demultiplexer, named Ultraplex, which splits a raw FASTQ file containing barcodes either at a single end or at both 5’ and 3’ ends of reads, trims the sequencing adaptors and low-quality bases, and moves unique molecular identifiers (UMIs) into the read header, allowing subsequent removal of PCR duplicates. Ultraplex is able to perform such single or combinatorial demultiplexing on both single- and paired-end sequencing data, and can process an entire Illumina HiSeq lane, consisting of nearly 500 million reads, in less than 20 minutes. Conclusions: Ultraplex greatly reduces computational burden and pipeline complexity for the demultiplexing of complex sequencing libraries, such as those produced by various CLIP and ribosome profiling protocols, and is also very user friendly, enabling streamlined, robust data processing. Ultraplex is available on PyPi and Conda and via Github.

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Database construction for Vietnamese catfish genome

Vietnam Journal of Biotechnology ◽

10.15625/1811-4989/17/3/13603 ◽

2020 ◽

Vol 17 (3) ◽

pp. 449-454

Author(s):

Nguyen Hoang Vu ◽

Nguyen Thanh Phuong ◽

Le Thi Nguyen Binh ◽

Kim Thi Phuong Oanh

Keyword(s):

Biological Research ◽

Web Browsers ◽

Sequencing Data ◽

Pangasianodon Hypophthalmus ◽

Database Construction ◽

Genomic Studies ◽

Data Organisation ◽

Next Generation Sequencing Ngs ◽

Convenient Interface ◽

Generation Sequencing

Molecular biological research plays an important role in aquaculture, contributes to the improvement ofbroodstocks efficiently. Recently, with the development of next-generation sequencing (NGS) technology,genomic studies have been rapidly increased, in which data organisation and management hold a crucialposition. After obtaining NGS sequencing data of Vietnamese catfish (Pangasianodon hypophthalmus), wehave analysed and annotated the catfish genome, from which we have constructed a database for efficientusage. The database is built upon open source software following a three-layer model (interface, Web serviceand database) with a convenient interface through Web browsers. Users can look up sequence and annotationdata as well as visualize sequences through the Jbrowse genome browser. This database is important resourcefor functional genome and genetic improvement of the catfish.

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