BOL: Related items

CLgenomics

Radha Agarkar — Fri, 03 Mar 2017 09:57:28 -0600

CLgenomics is a standalone desktop software specifically designed for bacterial genome analysis. This program has a powerful multi-genome browser, which enables rapid and responsive exploration of bacterial genomes.

To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics). Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers >40,000 CLG files of publicly available Bacterial and Archaeal genomes.

Address of the bookmark: https://chunlab.wordpress.com/clgenomics-software/

SeqMule: Automated human exome/genome variants detection

Abhimanyu Singh — Tue, 07 Mar 2017 10:12:36 -0600

SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file. SeqMule also has some built-in functions, such as pooling consensus calls from various callers, plotting a Venn diagram showing intersection among different callers, and downloading databases. SeqMule can be used for both Mendelian disease study and cancer genome study.

Address of the bookmark: http://seqmule.openbioinformatics.org/en/latest/

EUKulele: Taxonomic annotation of the unsung eukaryotic microbes

Shruti Paniwala — Sat, 26 Dec 2020 12:10:17 -0600

EUKulele, an open-source software tool designed to assign taxonomy to microeukaryotes detected in meta-omic samples, and complement analysis approaches in other domains by accommodating assembly output and providing concrete metrics reporting the taxonomic completeness of each sample.

Address of the bookmark: https://github.com/AlexanderLabWHOI/EUKulele

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Neel — Tue, 30 Nov 2021 23:23:57 -0600

MitoZ, consisting of independent modules of de novo assembly, findMitoScaf (find Mitochondrial Scaffolds), annotation and visualization, that can generate mitogenome assembly together with annotation and visualization results from HTS raw reads.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

Linux Cheat Sheet

Jitendra Narayan — Tue, 09 Jul 2013 17:30:04 -0500

In an attempt to find a good Linux reference for bioinformatician and BOL readers, I was unsuccessful at finding a decent one on the Internet. So, we decided to make a cheat sheet for biological programmers.

JSON

Abhimanyu Singh — Tue, 04 Apr 2017 08:02:39 -0500

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.

JSON is built on two structures:

A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.
An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence.

These are universal data structures. Virtually all modern programming languages support them in one form or another. It makes sense that a data format that is interchangeable with programming languages also be based on these structures.

Address of the bookmark: http://json.org/

Bioinformatics: Introduction to PERL

Archana Malhotra — Thu, 11 Jul 2013 09:49:37 -0500

This course is aimed at those new to programming and provides an introduction to programming using Perl. By the end of this course, attendees should be able to write simple Perl programs and to understand more complex Perl programs written by others. The course will be taught using the online Learning Perl materials created by Sofia Robb of the University of California Riverside. Further information is available.

Lifemap

Jit — Mon, 10 Apr 2017 05:42:37 -0500

Lifemap is an interactive tool to explore the WHOLE NCBI TAXONOMY. The concept used in Lifemap is similar to the one used in cartography with tools like Google Maps© or Open Street Maps: exploring is done by zooming and panning.

The current tree contains ALL species present in NCBI taxonomy as of October 18th, 2016: 1,135,169 species including 10,545 Archaea, 418,777 Bacteria and 705,847 Eukaryotes. The Lifemap tree is updated every two weeks.

All the nodes in the tree are clickable. This displays various information and options:

The species name (and the associated common name if there is one)
The rank (kingdom, family, class, species...)
Ability to go to the corresponding node/species on NCBI web site (displayed in a new window)
Possibility to download the corresponding subtree in newick extended format
Possibilty to get the whole lineage from the current node/tip to the root of the tree.

Address of the bookmark: http://lifemap-ncbi.univ-lyon1.fr/

NGS & Bioinformatics Summit Europe

Sat, 13 Jul 2013 17:02:34 -0500

NGS & Bioinformatics Summit Europe

Conference

7th to 8th October 2013
Berlin, Germany

Website: https://www.gtcbio.com/conference/ngseurope-overview
Contact person: Kristen Starkey

We welcome you to join us at GTC’s NGS & Bioinformatics Summit Europe on October 7-8, 2013 in Berlin, Germany.

Organized by: GTC
Deadline for abstracts/proposals: 7th September 2013

ChromHMM: Chromatin state discovery and characterization

Abhimanyu Singh — Wed, 19 Apr 2017 04:06:23 -0500

ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

Address of the bookmark: http://compbio.mit.edu/ChromHMM/