BOL: Related items

Kraken: ultrafast metagenomic sequence classification using exact alignments

Jit — Mon, 27 Jun 2016 11:01:44 -0500

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

Krona

https://sourceforge.net/p/krona/home/krona/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/

Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory

Wed, 18 Aug 2021 00:19:48 -0500

Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

Understanding DUMP files from NCBI Taxonomy database !

Shruti Paniwala — Fri, 15 Jul 2022 04:29:05 -0500

*.dmp files are bcp-like dump from GenBank taxonomy database

General information.

Field terminator is "\t|\t"

Row terminator is "\t|\n"

nodes.dmp file consists of taxonomy nodes. The description for each node includes the following

fields:

tax_id -- node id in GenBank taxonomy database

parent tax_id -- parent node id in GenBank taxonomy database

rank -- rank of this node (superkingdom, kingdom, ...)

embl code -- locus-name prefix; not unique

division id -- see division.dmp file

inherited div flag (1 or 0) -- 1 if node inherits division from parent

genetic code id -- see gencode.dmp file

inherited GC flag (1 or 0) -- 1 if node inherits genetic code from parent

mitochondrial genetic code id -- see gencode.dmp file

inherited MGC flag (1 or 0) -- 1 if node inherits mitochondrial gencode from parent

GenBank hidden flag (1 or 0) -- 1 if name is suppressed in GenBank entry lineage

hidden subtree root flag (1 or 0) -- 1 if this subtree has no sequence data yet

comments -- free-text comments and citations

Taxonomy names file (names.dmp):

tax_id -- the id of node associated with this name

name_txt -- name itself

unique name -- the unique variant of this name if name not unique

name class -- (synonym, common name, ...)

Divisions file (division.dmp):

division id -- taxonomy database division id

division cde -- GenBank division code (three characters)

division name -- e.g. BCT, PLN, VRT, MAM, PRI...

comments

Genetic codes file (gencode.dmp):

genetic code id -- GenBank genetic code id

abbreviation -- genetic code name abbreviation

name -- genetic code name

cde -- translation table for this genetic code

starts -- start codons for this genetic code

Deleted nodes file (delnodes.dmp):

tax_id -- deleted node id

Merged nodes file (merged.dmp):

old_tax_id -- id of nodes which has been merged

new_tax_id -- id of nodes which is result of merging

Citations file (citations.dmp):

cit_id -- the unique id of citation

cit_key -- citation key

pubmed_id -- unique id in PubMed database (0 if not in PubMed)

medline_id -- unique id in MedLine database (0 if not in MedLine)

url -- URL associated with citation

text -- any text (usually article name and authors).

-- The following characters are escaped in this text by a backslash:

-- newline (appear as "\n"),

-- tab character ("\t"),

-- double quotes ('\"'),

-- backslash character ("\\").

taxid_list -- list of node ids separated by a single space

Bpipe - a tool for running and managing bioinformatics pipelines

Radha Agarkar — Sat, 21 May 2016 22:42:16 -0500

Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.

January 20th, 2016 - New! Bpipe 0.9.9 released!
Download latest, all
Documentation
Mailing List (Google Group)

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

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/

Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA

Thu, 19 Aug 2021 08:45:24 -0500

wist is looking for a Bioinformatics Scientist to join our Production Bioinformatics Team. You will work alongside research scientists, software engineers and data scientists to further deliver on our mission to expand access to best-in-class synthetic biology and next-generation sequencing applications. You will be developing and engineering tools to better evaluate and build hardened, production quality pipelines, optimize data quality, and automate lab and bioinformatics processes. Our ideal candidate is an organized problem solver with a background in developing and building novel production-quality bioinformatics tools and packages. Equally excellent communication skills and a proven ability to work independently are required.

More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

Amity University Bioinformatics Summer Program - Kolkata

eliabrodsky — Tue, 11 Jun 2019 21:27:10 -0500

Registrations are now open for the 2019 Summer Bioinformatics Training program at Amity University, Kolkata. The program will focus on introductory topics for life science students. We will review important history, topics and challenges bioinformatics can help address in the context of basic research, discovery and industry.

Read more: https://edu.t-bio.info/amity-university-summer-bioinformatics-program-registrations-are-open/

k-mers tutorial - classification and taxonomy

Neel — Thu, 26 Aug 2021 10:28:43 -0500

DNA k-mers underlie much of our assembly work, and we (along with many others!) have spent a lot of time thinking about how to store k-mer graphs efficiently, discard redundant data, and count them efficiently.

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

MEGAN6

Neel — Mon, 25 Jul 2016 05:45:22 -0500

Microbiome analysis using a single application

MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application.

Taxonomic analysis using the NCBI taxonomy or a customized taxonomy such as SILVA
Functional analysis using InterPro2GO, SEED, eggNOG or KEGG
Bar charts, word clouds, Voronoi tree maps and many other charts
PCoA, clustering and networks
Supports metadata
MEGAN parses many different types of input

Why use MEGAN6?

The software is:

Easy to use. MEGAN6 is a single application and all features are available through menus, toolbars and graphics. No scripting skills required.
Powerful. MEGAN6 allows you to work with hundreds of samples containing hundreds of millions of sequencing reads. Blast-like analysis can be performed using DIAMOND.
Comprehensive. MEGAN6 offers a large range of analysis tools, and is under active development.

Address of the bookmark: https://ab.inf.uni-tuebingen.de/software/megan6

Stay Connected and Productive: Unlock the Power of Screen, Tmux, and Mosh for Bioinformatics

BioStar — Wed, 22 Jan 2025 00:29:52 -0600

If you are a bioinformatician, chances are you have spent hours running long, complex analyses on remote servers only to lose your session because of an unstable connection. Frustrating, isnt it? Fear not! With tools like screen, tmux, and mosh, you can safeguard your workflow and stay productive, no matter where you are.

Why Remote Session Management is a Must-Have

In bioinformatics, tasks like genome assembly, RNA-seq analyses, and phylogenetic computations often take hours or days. A dropped SSH connection can result in:

Lost Progress: Restarting a job from scratch wastes valuable time.
Workflow Interruptions: Disruptions can derail your focus and productivity.
Corrupted Data: Interrupted processes may lead to incomplete or corrupted outputs.

By integrating screen, tmux, or mosh into your workflow, you can avoid these setbacks and ensure a seamless experience.

Screen: The Classic Workhorse

Screen is a terminal multiplexer that comes pre-installed on most Linux systems. It allows you to manage multiple terminal sessions and reconnect to them even after being disconnected.

Getting Started with Screen:

Start a Session:

screen
Detach from a Session:
Press Ctrl+A, then D.
Reattach to a Session:

screen -r

Pro Tip: Enhance your screen experience with a customized .screenrc configuration file. Download one here: Get .screenrc.

Tmux: A Modern Alternative

Tmux takes everything great about screen and adds modern features, including better key bindings and intuitive session management. It\u2019s perfect for bioinformaticians who want more control over their workflow.

Getting Started with Tmux:

Start a Session:

tmux
Detach from a Session:
Press Ctrl+B, then D.
Reattach to a Session:

tmux attach

Customize Your Tmux Experience:
Use a .tmux.conf file to personalize your setup. Grab one here: Download .tmux.conf.

Mosh: The Mobile Shell for Unreliable Connections

SSH works well for stable networks, but it struggles in areas with spotty connectivity. Enter Mosh, the Mobile Shell. Designed for intermittent networks, Mosh keeps your session alive even when the connection drops temporarily.

Why Mosh is a Game-Changer:

No lag over high-latency networks.
Automatically reconnects when the network is restored.
Ideal for working on the go, from cafes to trains.

Getting Started with Mosh:

Install Mosh:

sudo apt install mosh # For Debian/Ubuntu
Connect to a Server:

mosh username@server

Learn more at mosh.org.

Why This Matters for Bioinformatics

Every bioinformatician knows the value of time and data integrity. Tools like screen, tmux, and mosh provide a lifeline when running long analyses, enabling you to:

Safeguard your work against disconnections.
Easily manage multiple workflows in parallel.
Stay productive, even in challenging environments.

Quickstart Cheat Sheet

Screen:

screen # Start a session Ctrl+A, D # Detach screen -r # Reattach
Tmux:

tmux # Start a session Ctrl+B, D # Detach tmux attach # Reattach
Mosh:

mosh username@server

Final Thoughts

As a bioinformatician, your time is too valuable to spend restarting analyses due to technical hiccups. With screen, tmux, and mosh in your toolkit, you can work smarter, protect your progress, and stay productive no matter where you are. Start using these tools today and transform the way you work with remote systems.

Let me know how these tools work for you, and don\u2019t forget to follow for more bioinformatics tips!