<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/35429?offset=390 https://bioinformaticsonline.com/bookmarks/view/26322/liftover Mon, 08 Feb 2016 15:45:03 -0600 https://bioinformaticsonline.com/bookmarks/view/26322/liftover <![CDATA[liftover]]> Convenient conversions between genome assemblie. The liftover package makes it easy to remap genomic coordinates to a different genome assembly.

More at https://github.com/aaronwolen/liftover

https://www.bioconductor.org/help/workflows/liftOver/

Address of the bookmark: https://github.com/aaronwolen/liftover

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/27113/picard Fri, 29 Apr 2016 08:21:54 -0500 https://bioinformaticsonline.com/bookmarks/view/27113/picard <![CDATA[Picard]]> Picard is a set of command line tools for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF. These file formats are defined in the Hts-specs repository. See especially the SAM specification and the VCF specification.

Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on GitHub.

The Picard toolkit is open-source under the MIT license and free for all uses.

Enjoy!

Address of the bookmark: http://broadinstitute.github.io/picard/

]]> Neel https://bioinformaticsonline.com/bookmarks/view/27110/easyfig Fri, 29 Apr 2016 05:49:39 -0500 https://bioinformaticsonline.com/bookmarks/view/27110/easyfig <![CDATA[Easyfig]]> Easyfig has moved to github, for newer releases of Easyfig please visit our new webpage - https://mjsull.github.io/Easyfig.  Easyfig is a Python application for creating linear comparison figures of multiple genomic loci with an easy-to-use graphical user interface (GUI).

More at http://easyfig.sourceforge.net/

Address of the bookmark: http://easyfig.sourceforge.net/

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/26968/scalce Fri, 15 Apr 2016 05:09:51 -0500 https://bioinformaticsonline.com/bookmarks/view/26968/scalce <![CDATA[SCALCE]]> SCALCE (/skeɪlz/, a.k.a. boosting Sequence Compression Algorithms using Locally ConsistentEncoding) is a tool for compressing FASTQ files. It is designed specifically for the Illumina-generated FASTQ files, but supports any valid FASTQ with consistent read lengths. 

More at http://sfu-compbio.github.io/scalce/

Address of the bookmark: http://sfu-compbio.github.io/scalce/

]]> Surabhi Chaudhary https://bioinformaticsonline.com/bookmarks/view/27035/spades Tue, 19 Apr 2016 08:37:08 -0500 https://bioinformaticsonline.com/bookmarks/view/27035/spades <![CDATA[SPAdes]]> SPAdes – St. Petersburg genome assembler – is intended for both standard isolates and single-cell MDA bacteria assemblies. This manual will help you to install and run SPAdes. SPAdes version 3.7.1 was released under GPLv2 on March 8, 2016 and can be downloaded from http://bioinf.spbau.ru/en/spades.

Manual at http://spades.bioinf.spbau.ru/release3.7.1/manual.html

Address of the bookmark: http://bioinf.spbau.ru/spades

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis Tue, 26 Apr 2016 03:48:23 -0500 https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis <![CDATA[HOMER: Software for motif discovery and next-gen sequencing analysis]]> This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.

  1. Setting up your computing environment
  2. Retrieving and storing sequencing files (your own data or from public sources)
  3. Checking sequence quality, trimming, general sequence manipulation
  4. Mapping reads to a reference genome
  5. Manipulating SAM/BAM alignment files
  6. Visualizing data in a genome browser


RNA-Seq

  1. De novo transcript discovery and differential analysis with Cufflinks
  2. Differential expression analysis with R/Bioconductor
  3. Clustering of large expression datasets (microarray or RNA-Seq)


Microarray

  1. Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor

General Tips for Data Analysis

  1. Excel workarounds, adding gene annotation, X-Y plots tips, etc.

Address of the bookmark: http://homer.salk.edu/homer/basicTutorial/

]]> Neel https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes Wed, 27 Apr 2016 11:07:59 -0500 https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes <![CDATA[RASTtk : algorithm for building custom annotation pipelines and annotating batches of genomes]]> The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

More at http://www.nature.com/articles/srep08365

Address of the bookmark: http://rast.nmpdr.org/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/27331/andi Fri, 13 May 2016 05:16:35 -0500 https://bioinformaticsonline.com/bookmarks/view/27331/andi <![CDATA[Andi]]> This is the andi program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because andi does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.

This readme covers all necessary instructions for the impatient to get andi up and running. For extensive instructions please consult the manual.

More at https://github.com/evolbioinf/andi/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full

]]> Jit https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data Fri, 02 Feb 2018 03:24:28 -0600 https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data <![CDATA[karyoploteR: plot whole genomes with arbitrary data]]> karyoploteR is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.

Address of the bookmark: https://bernatgel.github.io/karyoploter_tutorial/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering Thu, 16 Jun 2016 17:55:17 -0500 https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering <![CDATA[CNIDARIA: fast, reference-free phylogenomic clustering]]> Motivation: Identification of biological specimens is a major requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but these do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.

Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on ge-nome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% accuracy at supra-species level and 78% accuracy for species level.

Availability and Implementation: Cnidaria is written in C++ and Python and is available at http://www.ab.wur.nl/cnidaria.

Contact: Saulo Aflitos - sauloal@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Address of the bookmark: https://github.com/sauloal/cnidaria/wiki

]]> Shruti Paniwala