BOL: Related items

Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome

BioStar — Wed, 22 Jul 2020 10:11:13 -0500

We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.

More at https://pubmed.ncbi.nlm.nih.gov/20494974/

Address of the bookmark: http://www.sequenceontology.org/cgi-bin/soba.cgi

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

A Beginner's Guide to Using Kraken for Taxonomic Classification

Neel — Fri, 13 Dec 2024 11:29:03 -0600

Kraken is a popular bioinformatics tool designed for fast and accurate taxonomic classification of metagenomic sequences. Its efficiency and precision make it a go-to resource for analyzing microbial communities, including bacteria, viruses, archaea, and fungi. Whether you're new to bioinformatics or experienced in the field, Kraken is an indispensable tool for taxonomic analysis.

In this blog, we’ll walk through the basics of Kraken, from installation to running an analysis, and highlight its key features and applications.

What is Kraken?

Kraken is a sequence classification tool that assigns taxonomic labels to DNA sequences using exact k-mer matching. It uses a reference database of genomes, dividing sequences into k-mers and identifying matches in a computationally efficient way.

Key Features of Kraken

Speed: Kraken processes data much faster than alignment-based methods.
Accuracy: It uses a precise k-mer matching algorithm for high-resolution taxonomic assignments.
Scalability: It can handle large metagenomic datasets.
Custom Databases: You can build and use custom databases tailored to your research needs.

Installing Kraken

System Requirements
- A Unix-based operating system (Linux/macOS).
- Sufficient computational resources for database building (RAM and disk space).
Installation Steps
- Clone the Kraken repository from GitHub:
  
  git clone https://github.com/DerrickWood/kraken.git cd kraken
- Compile the Kraken binaries:
  
  make
- Add Kraken to your PATH for easy access:
  
  export PATH=$PATH:/path/to/kraken

Preparing a Database

Kraken requires a database of reference genomes. You can use a pre-built database or create a custom one.

Downloading a Pre-built Database
Kraken offers pre-built databases, such as the MiniKraken database, which is lightweight and suitable for smaller datasets. Download it using:

kraken-build --download-library minikraken
Building a Custom Database
To include specific genomes, download FASTA files and build the database:

kraken-build --download-library bacteria --threads 4 --db my_database kraken-build --build --db my_database

This process may take considerable time and resources, depending on the size of the database.

Running Kraken

Once the database is ready, you can classify sequences.

Basic Usage
Use the following command to classify sequences:

kraken --db my_database --threads 4 --fastq-input input_sequences.fastq --output kraken_output.txt

Key options:
- --db: Specifies the database.
- --threads: Number of threads for parallel processing.
- --fastq-input: Indicates input file format (FASTQ/FASTA).
Interpreting Results
Kraken generates an output file with columns for sequence IDs, taxonomic classifications, and the confidence score.

Visualizing Kraken Results

Kraken results can be visualized using tools like Krona or converted to human-readable reports using kraken-report.

Generate a Report

kraken-report --db my_database kraken_output.txt > kraken_report.txt
Krona Visualization
Install Krona and convert Kraken output for visualization:

cut -f2,3 kraken_output.txt | ktImportTaxonomy -o krona_output.html

Open the HTML file in your browser to interactively explore the taxonomic classifications.

Advanced Usage

Confidence Thresholds
Adjust the confidence threshold for classification using the --confidence option. Higher values reduce false positives but may miss some true positives:

kraken --db my_database --confidence 0.1 --fastq-input input.fastq
Paired-End Reads
For paired-end sequencing data, use:

kraken --db my_database --paired reads_1.fastq reads_2.fastq
Customizing K-mers
Kraken allows you to set custom k-mer lengths during database building for specific applications.

Applications of Kraken

Microbial Ecology: Characterizing microbial communities in soil, water, and the human microbiome.
Pathogen Detection: Identifying pathogens in clinical samples.
Fungal Research: Analyzing fungal diversity in metagenomic datasets.
Environmental Monitoring: Tracking microbial populations in diverse habitats.

Conclusion

Kraken is a versatile and efficient tool for taxonomic classification in metagenomics. Its speed, accuracy, and flexibility make it a favorite among bioinformaticians. By following this guide, you can set up and use Kraken to unlock insights into microbial and fungal communities, paving the way for discoveries in ecology, medicine, and biotechnology.

GABi

Fri, 06 Dec 2013 16:43:01 -0600

GABi Research
The major researching fields defined as the GABi scope are described next:
Sequence Analysis
Protein Structure Prediction
Comparative Genomics
Functional Analysis of Residues on Protein Families
Gene/Protein Networks
Genome structure & base composition
Highthroughput data analysis from NGS

Lab Page http://gabi.cidbio.org/index/

How to sequence the human genome - Mark J. Kiel

Fri, 30 May 2014 13:24:11 -0500

View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence. Lesson by Mark J. Kiel, animation by Marc Christoforidis.

deepTools

Martin Jones — Sat, 08 Nov 2014 15:02:08 -0600

deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. To do so, deepTools contains useful modules to process the mapped reads data to create coverage files in standard bedGraph and bigWig file formats. By doing so, deepTools allows the creation of normalized coverage files or the comparison between two files (for example, treatment and control). Finally, using such normalized and standardized files, multiple visualizations can be created to identify enrichments with functional annotations of the genome.

Publicaton: http://nar.oxfordjournals.org/content/early/2014/05/05/nar.gku365.full

Source Code and Wiki: https://github.com/fidelram/deepTools/wiki

Galaxy Tool Shed repository: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools

and example Galaxy workflows: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools_workflows

Rosalind Bioinformatics problems !!!

Abhi — Thu, 18 Dec 2014 10:32:48 -0600

Rosalind is a platform for learning bioinformatics and programming through problem solving. Take a tour to get the hang of how Rosalind works.

http://rosalind.info/problems/list-view/

Address of the bookmark: http://rosalind.info/problems/list-view/

LASTZ

Abhi — Mon, 18 Apr 2016 04:41:55 -0500

LASTZ is a program for aligning DNA sequences, a pairwise aligner. Originally designed to handle sequences the size of human chromosomes and from different species, it is also useful for sequences produced by NGS sequencing technologies such as Roche 454.

More at http://www.bx.psu.edu/~rsharris/lastz/

Thesis: http://www.bx.psu.edu/~rsharris/rsharris_phd_thesis_2007.pdf

Address of the bookmark: http://www.bx.psu.edu/~rsharris/lastz/

Bioinformatics WalkIn at NII

Fri, 04 Sep 2015 21:48:15 -0500

ADVERTISEMENT OF WALK-IN-INTERVIEW

NAME OF THE POST : Bioinformatician (Part time 3 days in a week) (One Position only)

DURATION : One Year

NAME OF THE PROJECT : Next generation sequencing facility

EDUCATIONAL QUALIFICATIONS : At least a Masters degree in Bioinformatics and Bachelors degree in any stream of life sciences

REQUIREMENTS :

Around 5 years of experience and proven track record in next generation sequence data analysis (supported by publications in peer-reviewed journals), ability to analyze transcriptomics, Chip-seq, and small RNA –seq data.

: Should have the ability to analyze raw primary data generated by Illumina next generation sequencing platforms and create / troubleshoot custom analysis Pipelines.

Should have ability to handle all downstream secondary and tertiary data analysis using commercially available as well as open source softwares (transcriptomics, ChIP-seq, small RNA-seq)

Apart from these, the applicant should have knowledge of the following: Programming: Perl and Python. Operating system:

Linux and Windows. NGS Analysis tools: Maq, BWA, Bowtie, SAM tools, BEDTools, MACS, Galaxy, FastQC, Bismark, MEDIPS, Tophat, Cufflinks, AvadisNGS, CLC Genomics Workbench, Galaxy, BaseSpace, Trinity Statistics: Microsoft Excel and R. Database: MySQL Genome Browser: UCSC, Ensemble, IGV, IGB Motif Analysis Tools: MEME Suite, Transfac and RSAT Functional Annotation Tools: DAVID, GeneCodis, Gene Cards Networking Tools: Cytoscape

EMOLUMENTS : The incumbent will be paid a fee of Rs. 2000/- per sitting/ per day.

SCIENTIST NAME : Dr. Arnab Mukhopadhyay,

Staff Scientific V Next generation sequencing facility

SCIENTIST’S E-MAIL ID : arnab@nii.ac.in

WALK IN INTERVIEW ON : 18th September, 2015

REGISTRATION OF CANDIDATES: 10.30 AM to 11.00 AM

PLEASE NOTE- 1. CANDIDATE MAY FILL UP APPLICATION IN THE PRECRIBED FORMAT ALONG WITH NECESSARY DOCUMENTS FOR VERIFICATION. 2. APPLICATIONS CONTAINING INCOMPLETE INFORMATION SHALL NOT BE ENTERTAINED. 3. DATE OF PASSING THE EXAMINATIONS MUST BE INDICATED CLEARLY. 4. ONLY REGISTERED CANDIDATES WILL BE INTERVIEWED. 5. NO TA/DA WILL BE PAID FOR ATTENDING THE INTERVIEW PRESCRIBED FORM 1. NAME 2. FATHER’S NAME 3. MOTHER’S NAME 4. DATE OF BIRTH 5. SEX (MALE/FEMALE) 6. CATEGORY (SC/ ST/ OBC/ PH) 7. ADDRESS a. (CORRSPONDENCE) b. (PERMANENT) 8. E MAIL, TELEPHONE NO. & MOBILE No (if any) 9. ACADEMIC & PROFESSIONAL QUALIFICATIONS NAME OF EXAMINATION PASSED WITH SUBJECTS YEAR OF PASSING BOARD/ UNIVERSITY PERCENTAGE/ DIVISION REMARKS 10. PAST EXPERIENCE & PRESENT EMPLOYMENT, IF ANY 11. CANDIDATES SHOULD STATE CLEARLY WHETHER THEY HAVE BEEN AWARDED PH.D DEGREE OR THESIS HAS BEEN SUBMITTED. 12. HAVE YOU APPLIED FOR A POSITION EARLIER IN THE INSTITUTE? IF SO:- (1) THE DETAILS OF THE PROJECT AND PROJECT INVESTIGATOR (2) IF CALLED FOR INVERVIEW, RESULTS THEREOF

More at http://www1.nii.res.in/sites/default/files/walkininterview-18sept2015.pdf

Ensembl comparative genomics resources

Jitendra Narayan — Sun, 28 Feb 2016 17:10:20 -0600

The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.

Database URL: http://www.ensembl.org.

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/