BOL: Related items

iSeqQC: a tool for expression-based quality control in RNA sequencing

BioStar — Sun, 16 Feb 2020 08:47:17 -0600

iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.

http://cancerwebpa.jefferson.edu/iSeqQC/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8

Address of the bookmark: https://github.com/gkumar09/iSeqQC

SEASTAR: Systematic Evaluation of Alternative STArt site in RNA

BioStar — Thu, 13 Aug 2020 09:54:27 -0500

SEASTAR (Systematic Evaluation of Alternative STArt site in RNA) is a software package for Transcription Start Site (TSS) identification and quantification using only RNA-seq data. It assembles novel TSSs based only on RNA-Seq data and merges them with known TSSs from a public database. This package enables high-quality TSS identification that is comparable to the highly sophisticated CAGE technology. This package is particularly useful for finding novel TSSs that contribute to transcriptome complexity along with identifying differential promoter utilization.

version 1.0.0 - updates several descriptions and tests. To achieve v0.9.4, one can visit https://github.com/zhyqin/SEASTAR-0.9.4 for download.

Address of the bookmark: https://github.com/Xinglab/SEASTAR

Coronavirus Resources !

Neel — Wed, 25 Mar 2020 17:11:33 -0500

2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

Elastic BLAST !

Abhi — Tue, 06 Sep 2022 18:14:57 -0500

ElasticBLAST is a new way to BLAST large numbers of queries, faster and on the cloud. Here are the top three reasons you should use ElasticBLAST:

1. ElasticBLAST can handle much LARGER queries!

ElasticBLAST can search query sets that have hundreds to millions of sequences and against BLAST databases of all sizes.

2. ElasticBLAST is FASTER

ElasticBLAST distributes your searches across multiple cloud instances to process them simultaneously. The ability to scale resources in this way allows you to process large numbers of queries in a shorter time than you could with BLAST+.

3. ElasticBLAST is EASY to run on the cloud

ElasticBLAST is easy to set up using our step-by-step instructions (Amazon Web Services (AWS), Google Cloud Platform (GCP)) and allows you to leverage the power of the cloud. Once configured, it manages the software and database installation, handles partitioning of the BLAST workload among the various instances, and deallocates cloud resources when the searches are done.

ElasticBLAST also selects the instance (i.e., machine) type for you based on database size. Of course, you can also choose the instance type manually if you prefer.

Address of the bookmark: https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/

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!

Ancestral sequence reconstruction steps !

Surabhi Chaudhary — Fri, 18 May 2018 08:28:26 -0500

Ancestral sequence reconstruction (ASR) – also known as ancestral gene/sequence reconstruction/resurrection – is a technique used in the study of molecular evolution. The method consists of the synthesis of an ancestral gene and expression of the corresponding ancestral protein. The idea of protein 'resurrection' was suggested in 1963 by Pauling and Zuckerkandl. Some early efforts were made in the eighties-nineties, led by the laboratory of Steven A. Benner, showing the potential of this technique – one that only started to be fulfilled in the post-genomic era. Thanks to the improvement of algorithms and of better sequencing and synthesis techniques, the method was developed further in the early 2000s to allow the resurrection of a greater variety of and much more ancient genes. Over the last decade, ancestral protein resurrection has developed as a strategy to reveal the mechanisms and dynamics of protein evolution.

BEAST is the best way to predict the ancestral structure. but, I suggest following steps?

1- Alignments "Mafft - http://mafft.cbrc.jp/alignment/software/source.html"

mafft --maxiterate 1000 --reorder --thread 24 --genafpair Dataset.fasta > Dataset_Alig.fasta

2- Your dataset has a good phylogenetic signal, is possible to perform with Tree-Puzzle "http://www.tree-puzzle.de";

3 - This dataset which the saturation index, I perform with "http://dambe.bio.uottawa.ca/dambe.asp";

4- Has evidence of possible recombination in your dataset, the evaluate if this presence or absence, because this may to influence the grouping of clades, I perform with

---recombination

4.1- Phi-test, implemented in SplitTree4"http://www.splitstree.org", (.nex file)

4.2- GARD deployed in webserver in the DataMonkey "http://www.datamonkey.org/" - turning to the amino acid seaview -> view proteins -> save as ...) Ideally do a tree-based groups.

4.3- RDP4 for download and installation on Windows in "http://web.cbio.uct.ac.za/~darren/rdp.html"

4.4- Hyphy (Mac, Windows, Linux) in "http://hyphy.org/w/index.php/Download"

4.5- Path-o-Gen (temporal structure of a tree input file -> arquivo.tre)

These steps above, I call of pre-processing to inferences phylogenetic...

5- Perform phylogenetic tree, used Bayesian Inference with Molecular Clock, but is necessary Clock Testing:

- This step is performed with program Beast (Beauti, Beast and TreeAnnotator), and Tracer_v1.5 more FigTree to inspection.

- Tutorials: http://beast.bio.ed.ac.uk/tutorials

- Downloads: http://beast.bio.ed.ac.uk/downloads

Post doc in Computational Genetics and Genomics at CEINGE Biotecnologie Avanzate, Naples, Italy

Tue, 11 Feb 2014 08:06:47 -0600

We are seeking one motivated scientist to analyze genomics and transcriptomics data of a large collection of neuroblastoma tumors. The successful candidate will be part of a team of researchers with extensive expertise in genome cancer study. He/she will be involved in the analysis of DNA-seq, RNA-seq, ChIP-seq data using available methods running in R and UNIX environment.

Qualifications

PhD or Post-Graduated Master degree is required. Successful candidates will have some expertise in data analysis of NGS data by using methods running in R and UNIX environment. Familiarity with genome databases and browsers is required.

Application

Candidates should send a CV and a brief personal statement focusing on their skills and interests related to the research project.

Contacts

Start date: 1° April 2014
Salary on grant: 25,000 euros per year.
Contact Person (Referent): Mario Capasso
Ref. Email: mario.capasso@unina.it and achille.iolascon@unina.it
Tel: +39 081 3737889

17 Marie Curie PhD position available immediately

Tue, 23 Jun 2015 06:52:06 -0500

Kindly look into following webpage:
http://medhealth.leeds.ac.uk/info/1450/scholarships/1795/marie_curie_phd_training_network

The closing date for application will be 26 June 2015.

MetaPred2CS

Manisha Mishra — Fri, 03 Mar 2017 05:15:07 -0600

MetaPred2CS Web server is a meta-predictor based on Support Vector Machine (SVM) that combines 6 individual sequence based protein-protein interaction prediction methods to predict prokaryotic two-component system protein-protein interactions (PPIs). The methods implemented in MetaPred2CS are 2 co-evolutionary methods: in-silico two hybrid (i2h) and mirror tree (MT) methods and 4 genomics context based methods: phylogenetic profiling (PP), gene fusion (GF), gene neighbourhood (GN) and and gene operon methods (GO).

http://metapred2cs.ibers.aber.ac.uk/

Address of the bookmark: https://github.com/martinjvickers/MetaPred2CS

Anvio

Shruti Paniwala — Thu, 16 Jun 2016 18:15:41 -0500

In a nutshell

Anvi’o is an analysis and visualization platform for ‘omics data.

Please find the methods paper here: https://peerj.com/articles/1319/

Anvi’o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper

An analysis and visualization platform for 'omics data http://merenlab.org/projects/anvio

Paper https://peerj.com/articles/1839/

Address of the bookmark: https://github.com/meren/anvio