Address of the bookmark: https://github.com/envgen

Features

• Circular and linear views of genomes

• Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs

• Smooth zooming down to the sequence level

• Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)

• Save high resolution PNG maps up to 8000x8000px

• Fully documented API for interacting with CGView.js maps

Address of the bookmark: https://js.cgview.ca/

What Are lncRNAs?

lncRNAs are RNA transcripts longer than 200 nucleotides that do not code for proteins. Despite their non-coding nature, they play diverse roles in gene regulation, including chromatin remodeling, transcriptional control, and post-transcriptional processing. Unlike messenger RNAs (mRNAs), lncRNAs often function as scaffolds, decoys, or guides in cellular machinery, influencing biological processes such as cell differentiation, immune response, and even cancer metastasis.

Challenges in lncRNA Research

Identifying and understanding lncRNAs pose unique challenges:

1. High Sequence Variability: Unlike protein-coding genes, lncRNAs exhibit low sequence conservation across species, making functional predictions difficult.
2. Low Expression Levels: lncRNAs are often expressed at low levels, complicating their detection in transcriptomic data.
3. Diverse Functions: The multifunctional nature of lncRNAs requires advanced computational tools to decipher their roles in complex networks.

Bioinformatics: A Crucial Ally in lncRNA Research

Bioinformatics bridges the gap between raw biological data and meaningful insights, making it indispensable in lncRNA research. Here’s how:

1. Identification and Annotation

High-throughput sequencing technologies like RNA-seq generate vast amounts of data. Bioinformatics tools such as StringTie, Cufflinks, and HISAT2 help assemble and annotate lncRNAs from this data. Additionally, databases like NONCODE, LNCipedia, and Ensembl provide curated repositories of lncRNA sequences and annotations.

2. Functional Prediction

Bioinformatics algorithms predict the potential functions of lncRNAs by analyzing their interactions with DNA, RNA, and proteins. Tools like LncRNA2Function and RIblast utilize sequence motifs and secondary structure predictions to hypothesize about the roles of specific lncRNAs.

3. Network Construction

lncRNAs often act as regulatory hubs. Bioinformatics platforms such as Cytoscape enable the visualization of lncRNA-mediated networks, elucidating their roles in pathways like cell cycle regulation and apoptosis.

4. Epigenetic Studies

lncRNAs are known to interact with chromatin-modifying complexes, influencing gene expression epigenetically. Tools like ChIP-seq and ATAC-seq, combined with computational pipelines, identify these interactions and map them to the genome.

5. Clinical Applications

Bioinformatics aids in the discovery of lncRNA biomarkers for diseases like cancer and neurodegenerative disorders. Machine learning models analyze differential expression profiles, helping prioritize lncRNAs with therapeutic potential.

Case Study: lncRNAs in Cancer Research

lncRNAs such as HOTAIR and MALAT1 have been implicated in cancer progression. Bioinformatics analyses have revealed their roles in promoting metastasis and altering the tumor microenvironment. For example, transcriptome analysis in cancer patients identifies lncRNA expression signatures, enabling precision medicine approaches.

Future Directions

The fusion of bioinformatics with experimental biology is unlocking the secrets of lncRNAs. Advances in artificial intelligence, single-cell sequencing, and structural modeling promise to overcome current limitations. Here are some promising directions:

• Integrative Analysis: Combining multi-omics data to understand the interplay of lncRNAs with other biomolecules.
• CRISPR Screens: Leveraging bioinformatics to design CRISPR-based functional screens for lncRNAs.
• Therapeutic Development: Using bioinformatics to design lncRNA-based therapeutics, including antisense oligonucleotides and RNA interference tools.

Conclusion

lncRNAs are the hidden gems of the genome, and bioinformatics is the key to unearthing their full potential. As research progresses, lncRNAs could pave the way for novel diagnostics, targeted therapies, and personalized medicine, revolutionizing our approach to complex diseases.

The journey into the world of lncRNAs is only beginning, and bioinformatics will continue to play a pivotal role in decoding these molecular mysteries. Whether you’re a researcher, clinician, or bioinformatics enthusiast, the study of lncRNAs offers a fascinating frontier of discovery.

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

• Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
• Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

• New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
• Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
• Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

MFannot is a program for the annotation of mitochondrial and plastid genomes. It is a PERL wrapper around a set of diverse, external independent tools.

It makes intense use of RNA/intron detection tools including HMMER, Exonerate, Erpin and others.

http://megasun.bch.umontreal.ca/cgi-bin/mfannot/mfannotInterface.pl

Address of the bookmark: https://github.com/BFL-lab/Mfannot

.NET Bio

http://blogs.msdn.com/b/msr_er/archive/2011/10/18/microsoft-biology-foundation-evolves-into-new-toolkit-net-bio.aspx

A language-neutral bioinformatics toolkit built using the Microsoft 4.0 .NET Framework to help developers, researchers, and scientists.

AMPHORA ("AutoMated Phylogenomic infeRence Application")

http://wolbachia.biology.virginia.edu/WuLab/Software.html

Metagenomics analysis software

Anduril

http://www.anduril.org/anduril/site/

Component-based workflow framework for data analysis

Armadillo workflow platform

Tool for designing and executing phylogenetic workflows

AutoDock

http://autodock.scripps.edu/

suite of automated docking tools

Biochemical Algorithms Library (BALL)

http://www.ball-project.org/

C++ library and framework for molecular modeling and visualization designed for rapid prototyping

Bio4j

http://bio4j.com/

Bio4j is a bioinformatics platform and graph based database built around most data available in UniProt KB(Swiss-Prot + TrEMBL), Gene Ontology (GO), UniRef (50,90,100), RefSeq, NCBI taxonomy, and Expasy Enzyme DB

Bioclipse

www.bioclipse.net

Visual platform for chemo- and bioinformatics based on the Eclipse Rich Client Platform (RCP).

Bioconductor

http://www.bioconductor.org/

R (programming language) language toolkit

Bioinformatics Learning Tutorial (BLT)

http://sourceforge.net/projects/biotutorial/

Educational interactive tutorials and 3D animations for Replication, Transcription, and Translation

BioHaskell

http://biohaskell.org/

Haskell (programming language)

BioJava

http://biojava.org/wiki/Main_Page

Java (programming language)

BioMOBY

http://biomoby.org/

registry of web services

BioPerl

http://www.bioperl.org/wiki/Main_Page

Perl language toolkit

BioPHP

http://www.biophp.org/

PHP language toolkit

Biopython

http://biopython.org/wiki/Main_Page

Python language toolkit

BioRails

https://github.com/biorails

a data management system designed to support researchers in drug discovery

BioRuby

http://bioruby.org/

Ruby language toolkit

BioSmalltalk

https://code.google.com/p/biosmalltalk/

Smalltalk language toolkit

BioUno

http://www.biouno.org/

BioUno is a project that applies Continuous Integration tools and techniques in Bioinformatics. It uses Jenkins and its plug-in API to create biology workflows and manage computer clusters.

caCORE

ontologic representation environment

caArray

https://cabig-stage.nci.nih.gov/community/tools/caArray

ontologic representation environment

EMBOSS

http://emboss.sourceforge.net/

Suite of packages for sequencing, searching, etc.

Gaggle

https://www.gaggle.net/

A framework for interoperability between systems biology software

Galaxy

http://galaxyproject.org/

Scientific workflow and data integration system

GenePattern

http://www.broadinstitute.org/cancer/software/genepattern/

Scientific workflow system that provides access to more than 150 genomic analysis tools

GeWorkbench

http://wiki.c2b2.columbia.edu/workbench/index.php/Home

Genomic data integration platform

GMOD

http://www.gmod.org/wiki/Main_Page

Toolkit for addressing many common challenges at biological databases.

GeneProf

http://www.geneprof.org/GeneProf/

A web-based, bioinformatics software suite for the analysis of functional genomics experiments, e.g. RNA-seq or ChIP-seq.

GeneTalk

http://www.gene-talk.de/

Tool for filtering sequence variants in VCF files. Network for scientists and clinicians for expertise and knowledge exchange. Database of annotations aboute sequence variants with clinically relevant information.

GenGIS

http://kiwi.cs.dal.ca/GenGIS/Main_Page

Application that allows users to combine digital map data with information about biological sequences collected from the environment.

GenomeSpace

http://www.genomespace.org/

Centralized web application that provides data format transformations and facilitates connections with other bioinformatics tools

GENtle

http://directory.fsf.org/wiki/GENtle

An equivalent to the proprietary Vector NTI, a tool to analyze and edit DNA sequence files

Integrated Genome Browser

http://bioviz.org/igb/

Java-based desktop genome browser

Integrative Genomics Viewer (IGV)

http://www.broadinstitute.org/igv/

High-performance desktop tool for interactive visual exploration of diverse genomic data

IntAct

http://www.ebi.ac.uk/intact/

molecular interaction database

InterMine

http://intermine.github.io/intermine.org/

Extensive data warehouse system for the analysis and integration of biological datasets

Java Treeview

http://jtreeview.sourceforge.net/

microarray data viewer

LabKey Server

http://labkey.com/

platform for integrating, analyzing and sharing data

OpenClinica

https://www.openclinica.com/

software for capturing and managing data in clinical trials

PromKappa

http://xbioinformatics.wordpress.com/tag/promkappa/

PromKappa (Promoter analysis by Kappa) software program used for promoter pattern generation and promoter analysis.

MeV: Multi-Experiment Viewer

http://www.tm4.org/mev.html

a desktop application for the analysis, visualization and data-mining of large-scale genomic data

PathVisio

http://www.pathvisio.org/

a desktop software for drawing, analysis and visualization of biological pathways

REDCRAFT

software for determining tertiary protein structure given assigned Residual Dipolar Coupling data

SAM Tools

Data format (SAM) and accompanying tool suite, for storing large nucleotide sequence alignments

Staden Package

Sequence assembly, editing and analysis, primarily consisting of gap4, gap5 and spin.

STAMP

Software package for analyzing metagenomic profiles that promotes ‘best practices’ in choosing appropriate statistical techniques and reporting results.

supraHex

An open-source R/Bioconductor package for omics data analysis using a supra-hexagonal map

Taverna workbench

Tool for designing and executing workflows

TGAC Browser

Genome Browser, visualisation solutions for big data in the genomic era

T-REX WebServer

Bioinformatics and phylogenetics webserver (NJ, PhyML, RAxML, MAFFT, MUSCLE, Newick viewer, Horizontal gene transfer detection, Reticulograms, Substitution models)

UGENE

integrated bioinformatics tools

Visomics

bioinformatics tools for omics data

Genome Analysis Toolkit 1.0 (GATK 1.0)

a software package to analyse next-generation resequencing data

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

The Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab) is hosted at the Istituto per le Applicazioni del Calcolo "Mauro Picone" - CNR . The laboratory has been officially opened in 2012 with the support of Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013 (PON "R&C"), and it incorporates several expertise and research activities started since 2007, and supported by several CNR projects. Main interest of BioinfoLab is to develop novel statistical methods and computational tools for the analysis of high dimensional data arising from "Multi-omics" applications. In particular, current activities involve the analysis of ChIP-seq and RNA-seq experiments.

More at http://bioinfo.na.iac.cnr.it/BioinfoLab/index.html

Arlequin: http://cmpg.unibe.ch/software/arlequin3/

BEAGLE: http://faculty.washington.edu/browning/beagle/beagle.html

fastPHASE: http://stephenslab.uchicago.edu/software.html

GENEHUNTER: http://linkage.rockefeller.edu/soft/gh/

The Genome Analysis Toolkit:

http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit

IMPUTE2: https://mathgen.stats.ox.ac.uk/impute/impute_v2.html

MACH: http://www.sph.umich.edu/csg/abecasis/MACH/

MERLIN: http://www.sph.umich.edu/csg/abecasis/Merlin/

PHASE: http://stephenslab.uchicago.edu/software.html

PL-EM: http://www.people.fas.harvard.edu/~junliu/plem/

“Read-backed phasing” algorithm: http://www.broadinstitute.org/gsa/wiki/index.php/Read-backed_phasing_algorithm

SHAPE-IT: http://www.griv.org/shapeit/

Address of the bookmark: http://engr.case.edu/li_jing/papers/00798gpattern.pdf