BOL: Related items

List of visualization tools for genome alignments

Rahul Nayak — Fri, 02 Feb 2018 13:25:33 -0600

Genome browsers are useful not only for showing final results but also for improving analysis protocols, testing data quality, and generating result drafts. Its integration in analysis pipelines allows the optimization of parameters, which leads to better results. But sometime, we need publication ready figure of genomes. Following are the list of genome alignment visualization tools, which could be useful for analysis and interpretation of results:

ABySS Explorer

Interactive Java application that uses a novel graph-based representation to display a sequence assembly and associated metadata

http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer

BamView

Genome browser and annotation tool that allows visualization of sequence features, next-generation sequencing (NGS) data and the results of analyses within the context of the sequence, and also its six-frame translation

http://www.sanger.ac.uk/resources/software/artemis/

DNannotator

Annotation web toolkit for regional genomic sequences

http://bioapp.psych.uic.edu/DNannotator.htm

JVM

Java Visual Mapping tool for NGS reads

http://www.springer.com/cda/content/document/cda_downloaddocument/9789401792448-c2.pdf?SGWID=0-0-45-1487072-p176815501

LookSeq

Web-based visualization of sequences derived from multiple sequencing technologies. Low- or high-depth read pileups and easy visualization of putative single nucleotide and structural variation

http://lookseq.sourceforge.net

MagicViewer

Visualization of short read alignment, identification of genetic variation and association with annotation information of a reference genome

http://bioinformatics.zj.cn/magicviewer/

MapView

Alignments of huge-scale single-end and pair-end short reads

http://omictools.com/mapview-s1367.html

MultiPipMaker

Computes alignments of similar regions in two DNA sequences. The resulting alignments are summarized with a ‘percent identity plot’ (pip)

http://pipmaker.bx.psu.edu/pipmaker/

PileLineGUI

Handling genome position files in NGS studies

http://sing.ei.uvigo.es/pileline/pilelinegui.html

SAMtools tview

Simple and fast text alignment viewer; NGS compatible

http://www.htslib.org/

SEWAL

Uses a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run

http://www.sourceforge.net/projects/sewal

STAR

A web-based integrated solution to management and visualization of sequencing data

http://wanglab.ucsd.edu/star/browser

SVA

Software for annotating and visualizing sequenced human genomes

http://www.svaproject.org

Viewer (IGV)

Visualization of large heterogeneous datasets, providing a smooth and intuitive user experience at all levels of genome resolution

https://www.broadinstitute.org/igv/

ZOOM Lite

NGS data mapping and visualization software

http://bioinfor.com/zoom/lite/

pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file

Jit — Thu, 24 May 2018 10:06:52 -0500

pbalign aligns PacBio reads to reference sequences, filters aligned reads according to user-specific filtering criteria, and converts the output to either the SAM format or PacBio Compare HDF5 (e.g., .cmp.h5) format. The output Compare HDF5 file will be compatible with Quiver if --forQuiver option is specified.

Address of the bookmark: https://github.com/PacificBiosciences/pbalign

RNA-seq Analysis Workshop Course Materials

Jit — Tue, 03 Jul 2018 08:14:14 -0500

RNAseq can be roughly divided into two "types": Reference genome-based - an assembled genome exists for a species for which an RNAseq experiment is performed. It allows reads to be aligned against the reference genome and significantly improves our ability to reconstruct transcripts. This category would obviously include humans and most model organisms but excludes the majority of truly biologically intereting species (e.g., Hyacinth macaw); Reference genome-free - no genome assembly for the species of interest is available. In this case one would need to assemble the reads into transcripts using de novo approaches. This type of RNAseq is as much of an art as well as science because assembly is heavily parameter-dependent and difficult to do well. In this lesson we will focus on the Reference genome-based type of RNA seq. http://chagall.med.cornell.edu/RNASEQcourse/

Address of the bookmark: http://chagall.med.cornell.edu/RNASEQcourse/

Convert EnsEMBL GTF to Annotation table (Geneid, GeneSymbol, GeneWiseChrLocation, GeneClass, Strand) Raw

EagleEye — Fri, 24 Jun 2016 18:08:49 -0500

Bash Script source:

https://gist.github.com/santhilalsubhash/367befcf5216be4b1fd9

Information:

This script converts EnsEMBL GTF (Ex: https://gist.githubusercontent.com/santhilalsubhash/1e7cca357e52a181dc25/raw/cfb803e07900a2baefbb6534f1299fd30cb57a29/sample.GTF) file to annotation table format. It generated two files
1) Transcript wise chromosome location with information about transcripts (Ex: https://gist.githubusercontent.com/santhilalsubhash/c7dec516e0338503a4b6/raw/de0af1a39f0005c4ce7321c5ae57fc8b4a14c7f4/sample.GTF_enst_annotation.txt)
2) Gene wise chromosome location with information about genes (Ex: https://gist.githubusercontent.com/santhilalsubhash/c92006c5080f0333bec2/raw/d16e0b2440d73b09b486d3c9751cdb248a73fa0b/sample.GTF_ensg_annotation.txt)

Note: You can download GTF files from http://www.ensembl.org/info/data/ftp/index.html

ComparativeGenomics Exercise2

Neel — Wed, 22 Aug 2018 22:10:56 -0500

COMPARATIVE MICROBIAL GENOMICS ANALYSIS WORKSHOP @ cbs.dtu.dk

Free Bioinformatics workbench https://www.mn.uio.no/ifi/english/research/networks/clsi/earlier_seminars/2012/tammivesth_osloseminarfinal.pdf

AMStat: display statistics of large sequence files from next generation sequencing projects

Neel — Fri, 09 Nov 2018 13:34:56 -0600

SAMStat is an efficient C program to quickly display statistics of large sequence files from next generation sequencing projects. When applied to SAM/BAM files all statistics are reported for unmapped, poorly and accurately mapped reads separately. This allows for identification of a variety of problems, such as remaining linker and adaptor sequences, causing poor mapping. Apart from this SAMStat can be used to verify individual processing steps in large analysis pipelines.

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

snakepipes: A toolkit based on snakemake and python for analysis of NGS data

Jit — Thu, 30 May 2019 04:06:13 -0500

snakePipes are flexible and powerful workflows built using snakemake that simplify the analysis of NGS data.

DNA-mapping*
ChIP-seq*
RNA-seq*
ATAC-seq*
scRNA-seq
Hi-C
Whole Genome Bisulfite Seq/WGBS

(*Also available in "allele-specific" mode)

snakePipes can be installed via conda :

'conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'.

Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.

Address of the bookmark: https://github.com/maxplanck-ie/snakepipes

RaGOO: Fast Reference-Guided Scaffolding of Genome Assembly Contigs

Jit — Sun, 27 Oct 2019 00:57:23 -0500

Alonge M, Soyk S, Ramakrishnan S, Wang X, Goodwin S, Sedlazeck FJ, Lippman ZB, Schatz MC: Fast and accurate reference-guided scaffolding of draft genomes. bioRxiv 2019.

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
Intact ordering and orienting of contigs.
Misassembly correction
GFF lift-over
Structural variant calling with and integrated version of Assemblytics
Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

Scientist-B (Bioinformatics) at AIIMS, Delhi

Sun, 20 Dec 2020 04:34:55 -0600

Name of the Project: “Artificial intelligence in Oncology, Harnessing big data and advanced computing to provide personalized diagnosis and treatment for Cancer patients”

Age Limit: 35

How to Apply for the AIIMS Life Science Job:

Interested applicants are asked to send out a detailed CV to Dr Ashok Sharma (aioncoaiims@gmail.com). Laboratory of Chromatin and also Cancer Epigenetics, Department of Biochemistry with the subject line “Application for Scientist-B position for MeitY project” latest by January 01st, 2021.
Complete Information of the year of passing, experience, marks, etc. ought to be mentioned in the CV Incomplete. applications will certainly be rejected Just shortlisted applicants will be called for interview. Chosen candidates will certainly be intimated by email/phone.
No TA/DA will certainly be paid for appearing in the interview.
Note, The institute reserved the right to fill up or not to fill up the post advertised.

Emoluments: Rs. 56,000/- plus 24 percent HRA

Eligibility:
2nd class Master’s Degree with a PhD in a pertinent subject (Bioinformatics) from.a recognized University
1st class Master’s degree in Life Sciences (Bioinformatics) from a recognized university OR.
Bachelor’s Degree in Engineering or-Technology with minimal 60% marks from a recognized University or equivalent.

Desirable Qualifications:
Experience in Bioinformatics/NGS data. Analysis/System Biology/Computer Science/ statistics with experience in Machine learning/Al project.
Experience of Deep learning applications in biological data ( image/text).
Proficient in Rf Python machine learning libraries.
Prior experience in the cancer-related project (ML-based) will be advantageous.
Experience with PyTorch/TensorFlow will certainly be very desirable.
Applicant should have strong scientific writing as well as. verbal abilities.
Papers in sci-indexed journals demonstrating ML skill sets.
Database handling will certainly be plus yet not required.

More detail at https://www.aiims.edu/images/pdf/recruitment/advertisement/biochem-16-12-20.pdf

Postdoctoral scientist genome analytics/ genome bioinformatics (m/f/*)

Sun, 16 Feb 2020 02:57:40 -0600

https://www.uksh.de/jobs/Stellenangebote-nr-20190570-p-8.html
Your profile:
Degree in bioinformatics, biostatistics, or equivalent
Experience in the processing and analysis of large-scale genomics data using compute clusters / high-performance computing
Strong competence in working in Unix/Linux environments (shell)
Strong programming skills (in particular: Python, R, Perl)
Experience with using git and snakemake
Fluent English language skills, both spoken and written
Strong communication skills and motivation to work in a young, interdisciplinary, dynamic team

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.