CABOG is one of many software programs called genome assemblers. These programs exist to overcome the fundamental limitation of all sequencing machines, namely, that they read out very few DNA letters at a time. These programs reconstruct genomes that are billions of letters long from the hundreds of letters per read that modern sequencers provide. What these programs do is often described as a scaled up version of a family solving a jigsaw puzzle.

The CABOG software was the first to accomplish many scientific goals. It was the first to assemble the genome of a multicellular organism (Drosophila melanogaster, 2000). It was the first to assemble both parental haplotypes of one human genome (J. Craig Venter, 2007). It was the first to assemble environmental sequence from the oceans (Sargasso Sea in 2004 and Global Ocean Sampling in 2007). It was first to combine reads from first-generation Sanger sequencing machines and second-generation pyrosequencing machines (Marine microbes, 2006). Today, CABOG is one of the leading assembly programs for data sets that include paired end data from the Roche 454 line of sequencing machines.

Address of the bookmark: http://www.jcvi.org/cms/research/projects/cabog/overview/

Kindly log on to conference website http://rgcb.res.in/IACR2014 for further details and timely updates and registration. We shall truly appreciate if the same be circulated among your friends, scholars and students encouraging them to participate in the meet.

http://210.212.237.38/iacrconference/

Classificier of reads from metagenomic sequencing experiments.

•  Kawulok, J., Deorowicz, S., CoMeta: Classification of Metagenomes Using k-mers, PLOS ONE, 2015; 10(4):1–23,

CoMSA

Compressor of multiple sequence alignments of proteins.

•  Deorowicz, S., Walczyszyn, J., Debudaj-Grabysz, A., CoMSA: compression of protein multiple sequence alignment files, Bioinformatics, 2019; 35(2):22–234,

DSRC

Compressor of sequencing reads.

•  Roguski, L., Deorowicz, S., DSRC 2: Industry-oriented compression of FASTQ files, Bioinformatics, 2014; 30(15):2213–2215,
•  Deorowicz, S., Grabowski, Sz., Compression of DNA sequences in FASTQ format, Bioinformatics, 2011; 27(6):860–862,

FAMSA

Multiple sequence alignment designed for huge families of proteins (even containing hundreds of thousands of sequences).

•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., FAMSA: Fast and accurate multiple sequence alignment of huge protein families, Scientific Reports, 2016; 6(33964):

FaStore

Compressor of FASTQ files.

•  Roguski, L., Ochoa, I., Hernaez, M., Deorowicz, S., FaStore - a space-saving solution for raw sequencing data, Bioinformatics, 2018; 34(16):2748–2756,

FQSqueezer

Experimental high-end compressor of FASTQ files.

•  Deorowicz, S., FQSqueezer: k-mer-based compression of sequencing data, Scientific Reports, 2020; 10(578):

GDC

Compressor of collections of genome sequences.

•  Deorowicz, S., Danek, A., Niemiec, M., GDC 2: Compression of large collections of genomes, Scientific Reports, 2015; 5(11565):1–12,
•  Deorowicz, S., Grabowski, Sz., Robust relative compression of genomes with random access, Bioinformatics, 2011; 27(21):2979–2986,

GTC

Genotype databases compressor with support for fast queries.

•  Danek, A., Deorowicz, S., GTC: how to maintain huge genotype collections in a compressed form, Bioinformatics, 2018; 34(11):1834–1840,

GTShark

Genotypes compressor.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, Bioinformatics, 2019; 35(22):4791–4793,

KMC

Memory frugal k-mer counter.

•  Kokot, M., Długosz, M., Deorowicz, S., KMC 3: counting and manipulating k -mer statistics, Bioinformatics, 2017; 33(17):2759–2761,
•  Deorowicz, S., Kokot, M., Grabowski, Sz., Debudaj-Grabysz, A., KMC 2: Fast and resource-frugal k-mer counting, Bioinformatics, 2015; 31(10):1569–1576,
•  Deorowicz, S., Debudaj-Grabysz, A., Grabowski, Sz., Disk-based k-mer counting on a PC, BMC Bioinformatics, 2013; 14():Article no. 160,

Kmer-db

Tool for estimation of evolutionary distances in a collection of genomes.

•  Deorowicz, S., Gudys, A., Dlugosz, M., Kokot, M., Danek, A., Kmer-db: instant evolutionary distance estimation, Bioinformatics, 2019; 35(1):133–136,

MuGI

Index allowing queries for a collection of multiple genome sequences.

•  Danek, A., Deorowicz, S., Grabowski, Sz., Indexes of Large Genome Collections on a PC, PLOS ONE, 2014; 9(10):e109384,

ORCOM

Experimental compressor of sequencing reads.

•  Grabowski, Sz., Deorowicz, S., Roguski, L., Disk-based compression of data from genome sequencing, Bioinformatics, 2014; 31(9):1389–1395,

PgSA

Index allowing queries for a collection of sequencing reads.

•  Kowalski, T., Grabowski, Sz., Deorowicz, S., Indexing arbitrary-length k-mers in sequencing reads, PLOS ONE, 2015; 10(7):1–16,

QuickProbs

Multiple sequence alignment designed especially for GPU.

•  Gudys, A., Deorowicz, S., QuickProbs 2: towards rapid construction of high-quality alignments of large protein families, Scientific Reports, 2017; 7(41553):
•  Gudys, A., Deorowicz, S., QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors, PLOS ONE, 2014; 9(2):e88901,

RECKONER

Read error corrector.

•  Maciej Długosz, M., Deorowicz, S., RECKONER: read error corrector based on KMC, Bioinformatics, 2017; 33(7):1086–1089,

TGC

Compressor of collections of genomes given in Variant Call Format (VCF) files.

•  Deorowicz, S., Danek, A., Grabowski, Sz., Genome compression: a novel approach for large collections, Bioinformatics, 2013; 29(20):2572–2578,

VCFShark

Compressor of VCF files.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, biorxiv.org, 2020; ():

Whisper

Experimental mapper of whole genome sequencing data.

•  Deorowicz, S., Gudys, A., Whisper 2: indel-sensitive short read mapping, bioRxiv.org, 2019; :
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Whisper: read sorting allows robust robust mapping of DNA sequencing data, Bioinformatics, 2019; 35(12):2043–2050,
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Robust mapping of whole genome sequencing data, Poster at The Biology of Genomes Conference, 2017;

the study of the sequence-structure relationship
(application -> structure prediction)
the study of the structure-function relationship
(application -> function prediction)

Therefore, anything related to the configuration (sequence) and conformation (structure) in atomic systems of proteins and nucleic acids, and the interaction of these with other elements (function) is of our major interest.

Lab page @ http://melolab.org/mbl/

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

Translational recoding.

RNA editing.

Evolution of the genetic code and translation.

More at http://lapti.ucc.ie/research.html

Lab page http://lapti.ucc.ie/index.html

Genome Workbench can display sequence data in many ways, including graphical sequence views, various alignment views, phylogenetic tree views, and tabular views of data. It can also align your private data to data in public databases, display your data in the context of public data, and retrieve BLAST results.

Genome Workbench is built on the NCBI C++ ToolKit and uses cross-platform APIs for graphics. It runs on your local machine, and is available for Windows 2000/XP, Linux, MacOS X, and various flavors of Unix.

NCBI Genome Workbench is an integrated application for viewing and analyzing sequence data. Genome Workbench was developed entirely in-house at NCBI and makes use of the NCBI C++ ToolKit. The C++ ToolKit provides a convenient and flexible cross-platform API for managing system internals, database connections, network sockets, and the NCBI data model. In addition, the C++ ToolKit provides the Object Manager, which abstracts handling of sequences and sequence-related objects.

 New Features in Genome Workbench 2.7.15

• Multiple Alignment View: implemented adaptive feature display when zooming in
• Active Objects Inspector replaces Selection Inspector. New View should offer an improved selection context examination. See Using Active Objects Inspector tutorial for more details.
• Binary packages for Linux OpenSUSE 13.1 are now available

Bug Fixes and Improvements in Genome Workbench 2.7.15

• Fixed major issue with OpenGL overlay/scrolling. Could cause crashes or view scrolling irregularities
• Multiple Pane View: fixed crash on loading BLAST results
• Graphical Sequence View: fixed crash on zooming in and out, related to SNP track
• Graphical Sequence View: fixed Go To Position dialog to give better diagnostics in case of a user error
• Graphical Sequence View: PDF export fixed rendering of Markers with commas in the name
• Text View / Flat File: fixed Mac OS rendering issues
• Text View / Flat File: performance optimization, extended capabilities of real-time rendering of molecules to tens of thousands
• File Import: optimization improvement to speed up load of files containing multiple project items
• File Import: remapping stage now shows accession.version and description of molecules, instead of plain GI numbers
• Mac OS: improved tooltips for toolbar buttons
• Phylogenetic Tree Builder Tool: improved diagnostics of errors
• Multiple Alignment View: optimizations to avoid main GUI freezes
• Open Dialog: removed duplicate elements in table of genomes (load Genome)
• PDF export: fixed issue with XREF table errors
• Tree View: fixed issues with showing Force Layout progress on Mac OS
• Tree View: PDF export fixed issues for showing labels of collapsed nodes
• Tree View: added an option to stop layout
• Tree View: broadcasting mechanism fixed not to accumulate selected nodes

Reference:

NCBI news

http://www.ncbi.nlm.nih.gov/tools/gbench/

Now, two research groups have engineered new optogenetic proteins that can be used to efficiently silence neurons. One of the two new proteins comes from the lab of Karl Deisseroth, a psychiatrist and neuroscientist at Stanford University who helped develop optogenetics as a research tool. His group’s new “off” switch for neurons was created by changing 10 of the 333 amino acids in an existing optogenetic protein, which itself had been engineered by combining natural proteins from green algae. That advance “creates a powerful tool that allows neuroscientists to apply a brake in any specific circuit with millisecond precision,” said Thomas Insel, director of the National Institute of Mental Health, in a released statement. The other new silencing protein, developed by scientists at the Humboldt University of Berlin and collaborators, was created by changing amino acids in the same existing optogenetic protein. 

Some researchers are also looking to optogenetics as a potential treatment for patients with a variety of conditions (see “For Mice, and Maybe Men, Pain Is Gone in a Flash,” and “Flipping on the Lights to Halt Seizures”) but there are huge challenges to overcome. The method requires genetic modification of cells to make them light-sensitive. It also requires implanted light sources for all but the shallowest of nerve endings.

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4– on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/ under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Address of the bookmark: https://dfast.nig.ac.jp/

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB