<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26290?offset=150 https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes Thu, 13 Aug 2020 10:06:19 -0500 https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes <![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]> PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/42946/aligngraph2-similar-genome-assisted-reassembly-pipeline-for-pacbio-long-reads Sun, 14 Mar 2021 09:42:47 -0500 https://bioinformaticsonline.com/bookmarks/view/42946/aligngraph2-similar-genome-assisted-reassembly-pipeline-for-pacbio-long-reads <![CDATA[AlignGraph2: similar genome-assisted reassembly pipeline for PacBio long reads]]> AlignGraph2 is the second version of AlignGraph for PacBio long reads. It extends and refines contigs assembled from the long reads with a published genome similar to the sequencing genome.

More at https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbab022/6146772

Address of the bookmark: https://github.com/huangs001/AlignGraph2

]]> Rahul Nayak https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps Fri, 10 Dec 2021 06:22:54 -0600 https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps <![CDATA[Illumina based assembly pipeline steps !]]> Illumina
  1. Merge re-sequenced FastQ files (cat)
  2. Read QC (FastQC)
  3. Adapter trimming (fastp)
  4. Removal of host reads (Kraken 2; optional)
  5. Variant calling
    1. Read alignment (Bowtie 2)
    2. Sort and index alignments (SAMtools)
    3. Primer sequence removal (iVar; amplicon data only)
    4. Duplicate read marking (picard; optional)
    5. Alignment-level QC (picard, SAMtools)
    6. Genome-wide and amplicon coverage QC plots (mosdepth)
    7. Choice of multiple variant calling and consensus sequence generation routes (iVar variants and consensus; default for amplicon data || BCFTools, BEDTools; default for metagenomics data)
      • Variant annotation (SnpEff, SnpSift)
      • Consensus assessment report (QUAST)
      • Lineage analysis (Pangolin)
      • Clade assignment, mutation calling and sequence quality checks (Nextclade)
      • Individual variant screenshots with annotation tracks (ASCIIGenome)
    8. Intersect variants across callers (BCFTools)
  6. De novo assembly
    1. Primer trimming (Cutadapt; amplicon data only)
    2. Choice of multiple assembly tools (SPAdes || Unicycler || minia)
  7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)
]]> Surabhi Chaudhary https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes Sat, 08 Jun 2024 16:25:08 -0500 https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes <![CDATA[Bactopia: a flexible pipeline for complete analysis of bacterial genomes]]> Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!

Bactopia was inspired by Staphopia, a workflow we (Tim Read and myself) released that is targeted towards Staphylococcus aureus genomes. Using what we learned from Staphopia and user feedback, Bactopia was developed from scratch with usability, portability, and speed in mind from the start.

Bactopia uses Nextflow to manage the workflow, allowing for support of many types of environments (e.g. cluster or cloud). Bactopia allows for the usage of many public datasets as well as your own datasets to further enhance the analysis of your sequencing. Bactopia only uses software packages available from Bioconda and Conda-Forge to make installation as simple as possible for all users.

To highlight the use of Bactopia and Bactopia Tools, we performed an analysis of 1,664 public Lactobacillus genomes, focusing on Lactobacillus crispatus, a species that is a common part of the human vaginal microbiome. The results from this analysis are published in mSystems under the title: Bactopia: a flexible pipeline for complete analysis of bacterial genomes

Bactopia Workflow

Address of the bookmark: https://bactopia.github.io/latest/

]]> Abhi https://bioinformaticsonline.com/blog/view/23582/integrative-rna-and-chip-seq-analysis-of-regulatory-t-cells Tue, 04 Aug 2015 05:03:19 -0500 https://bioinformaticsonline.com/blog/view/23582/integrative-rna-and-chip-seq-analysis-of-regulatory-t-cells <![CDATA[Integrative RNA and ChIP-Seq analysis of regulatory T-cells]]> Integrative RNA and ChIP-Seq analysis of regulatory T-cells , a Strand NGS application note describes how integrated multi-omics functionality in Strand NGS was used to find the regulatory role of FoxP3 in T-regulatory and T-helper cells. Learn how the gene expression profiles from RNA-Seq and FoxP3 DNA-protein binding sites from ChIP-Seq are integrated. For mor information, please write to us

]]> Strand https://bioinformaticsonline.com/bookmarks/view/34195/strand-life-sciences-announces-the-release-of-strand-ngs-v31-at-ashg-2017 Mon, 23 Oct 2017 02:36:05 -0500 https://bioinformaticsonline.com/bookmarks/view/34195/strand-life-sciences-announces-the-release-of-strand-ngs-v31-at-ashg-2017 <![CDATA[Strand Life Sciences announces the release of Strand NGS v3.1 at ASHG 2017]]> Strand Life Sciences announces the release of Strand NGS v3.1 at ASHG 2017

ORLANDO, USA, Oct 17, 2017/ PRNewswire/

Strand NGS now supports large scale RNA- and small-RNA-Seq and Unique Molecular Identifiers (UMIs) for DNA-, RNA-, and small-RNA-Seq.

Strand Life Sciences announced the latest version release of its bioinformatics flagship product, Strand NGS, at the Annual Meeting of the American Society of Human Genetics today. Two major themes in Strand NGS v3.1 address recent challenges in next generation sequencing (NGS).

The first theme is large-scale RNA-Seq data analysis. Current cross-cohort RNA- and small-RNA-Seq studies span tens of replicates and batches across hundreds of samples, sometimes conducted across several different institutions. For such studies, Strand NGS v3.1 includes confounding variable analysis to eliminate technical effects, including batch effects; the t-SNE plot; profile and heat-map plots of gene-body coverage; and several other notable visual enhancements.

The second new feature is support for Unique Molecular Identifiers, or UMIs, for DNA-, RNA- and small-RNA-Seq. UMI support in Strand NGS is end-to-end, spanning alignment to variant calling in DNA-Seq, and alignment to quantification in RNA- and small-RNA-Seq. The Bioo Scientific, Qiagen, and Rubicon UMI protocols are natively supported, and an intuitive interface allows the specification of custom UMI protocols.

“For liquid biopsies and low-grade FFPE samples, UMI support in DNA-Seq enables the detection of somatic variants at low concentrations. In RNA-Seq, large-scale and UMI support can be used in single-cell-based studies that reveal tumor-cell heterogeneity, even at low concentrations”, says Dr. Vamsi Veeramachaneni, Chief Scientific Officer, Strand Life Sciences.

“At Strand, we are continuously working towards improving the accuracy and efficiency of NGS data analysis. Customers can look forward to Strand NGS becoming available on the cloud in the near future”, says Dr. Ramesh Hariharan, Chief Executive Officer, Strand Life Sciences.

Visit Strand Life Sciences at ASHG booth #1017 to know more about Strand NGS v3.1 and other products and service offerings from Strand Life Sciences. Click here to access detailed agenda and v3.1 release notes.

About Strand Life Sciences

Strand Life Sciences is a premier life science informatics innovation company. Founded in 2000, Strand is a leader in technology innovations for healthcare using genomics. By enhancing sequence-based diagnostics and clinical genomic data interpretation using a strong foundation of computational, scientific, and medical expertise, Strand is bringing individualized medicine to the world. To know more, visit www.strandls.com

Address of the bookmark: http://www.strand-ngs.com/strand-announce-strandngss-v31

]]> Yeshodari https://bioinformaticsonline.com/blog/view/23586/calling-narrow-and-broad-peaks-from-chip-seq-data Tue, 04 Aug 2015 05:06:03 -0500 https://bioinformaticsonline.com/blog/view/23586/calling-narrow-and-broad-peaks-from-chip-seq-data <![CDATA[Calling narrow and broad peaks from ChIP-Seq data]]> Know about the state-of-the-art algorithms implemented in Strand NGS for detecting the binding sites of transcription factor (narrow peaks) and enriched regions of histone modification (broad peaks) from ChIP-Seq data.
Read the benchmarking study on Calling narrow and broad peaks from ChIP-Seq data in Strand NGS by Rohit Gupta and Anita Sathyanarayanan. For more information, please contact us

]]> Strand https://bioinformaticsonline.com/news/view/35991/webinar-on-diagnosis-of-rare-diseases-using-ngs-based-multi-gene-testing-case-studies-by-draparna-ganapathy-on-18-apr-2018 Mon, 19 Mar 2018 04:40:58 -0500 https://bioinformaticsonline.com/news/view/35991/webinar-on-diagnosis-of-rare-diseases-using-ngs-based-multi-gene-testing-case-studies-by-draparna-ganapathy-on-18-apr-2018 <![CDATA[Webinar on Diagnosis of Rare Diseases using NGS Based Multi-gene Testing- Case studies by Dr.Aparna Ganapathy on 18 Apr 2018]]> A disease is considered to be ‘rare’ when it affects one in about 2000 individuals in the population. This, individually are although rare, collectively, the incidence could be very high causing a significant socio-economic burden. Arriving at a confirmatory diagnosis is a major challenge in these inherited disorders, which can significantly impact treatment and disease management. Conventional genetic testing for rare diseases focuses mostly on sequencing of fewer genes, followed by a deletion/duplica-tion analysis by multiplex ligation-dependent probe amplifi¬cation (MLPA). This sequential testing strategy is time consuming and very expensive. Multi-gene panel based on NGS (next-generation sequencing) can allow us to detect all types of mutations, including large deletions/duplications, thus allowing us to perform a comprehensive genetic testing in a cost-effective manner. Thus, with the advent of NGS technology, the possibility of offering a ‘single platform solution’ for all types of genetic defects can become a reality.

The webinar will highlight some of the interesting case studies wherein multi-gene testing with NGS was helpful in arriving at a confirmatory as well as differential diagnosis, even for complex clinical conditions. With robust bioinformatic analysis, we were able to detect few complex variations in few cases which a conventional test had missed. Some of those cases will also be discussed.

Session 1: 9 am CET, 18 Apr 2018
Session 2: 8 am CET, 18 Apr 2018 
To attend, register here: http://www.strand-ngs.com/webinar_registration

About Speaker: Dr. Aparna Ganapathy is Senior scientist- Clinical Diagnostics at Strand Life Sciences. She has over 8 years of experience in human genetics and molecular biology. She received her Ph.D. in Human Molecular Genetics from Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore. At Strand Life Sciences, she is involved in the interpretation and clinical reporting of the genetic disorders. The focus of these genetic tests is to provide accurate and rapid clinical diagnosis for various inherited disorders.

]]> Strand https://bioinformaticsonline.com/opportunity/view/24178/essentials-of-statistics-and-data-analysis-using-r Mon, 31 Aug 2015 01:32:12 -0500 <![CDATA[Essentials of Statistics and Data Analysis using R]]> Clinical Development Services Agency (CDSA) is an extramural unit of Translational Health Science and Technology Institute (THSTI), Department of Biotechnology, Ministry of Science & Technology, Government of India. CDSA has a national mandate of strengthening capacity and capability building in the area of Clinical development and Translational Research.

CDSA is pleased to announce a 4 days hands-on training program on “Essentials of Statistics and Data Analysis using R” at ICGEB, Aruna Asaf Ali Road, New Delhi on December 1 – 4, 2015. This will involve developing and enhancing skills to understand basic principles of statistics for summarizing data and use of appropriate statistical tests as well as providing an understanding of data analysis using R. Didactic lectures with practical sessions will be delivered by experienced faculties from AIIMS and Novartis. Live classroom with power point presentations, case studies, mock exercise, practical sessions on R, group work with time for discussion and Q&A sessions are added advantages of this workshop.

Please contact gayatrivishwakarma.cdsa@thsti.res.in or vineetabaloni.cdsa@thsti.res.in for program and registration details.

Please nominate personage or register yourself on or before November 6, 2015 along with the electronic transfer of registration fee.

]]> https://bioinformaticsonline.com/researchlabs/view/860/the-centre-for-bioinformatics-mcb-lab Sun, 14 Jul 2013 12:41:20 -0500 <![CDATA[The Centre for Bioinformatics (MCB) Lab]]> The Centre for Bioinformatics (MCB) is a diverse collection of professors, postdoctoral fellows, and students, who share a common interest in Bioinformatics.

Research Area

We are interested in the development of the statistics and computational methods for the analysis of this data in breast cancer.
We have worked on probabilistic models for subcellular localization, protein-protein interactions, and problems related to chemical genomics.
We are interested in the development of bioinformatics/biostatistical methodology in the analysis of epigenetic/epigenomic data.
We are interested in integrative bioinformatics approaches to learn the gene, gene products, interactions, and regulatory mechanisms involved in mental retardation.

Link @ http://www.mcgill.ca/mcb/

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