Download 

http://arthropods.eugenes.org/EvidentialGene/trassembly.html

https://sourceforge.net/p/evidentialgene/blog/

Address of the bookmark: http://arthropods.eugenes.org/EvidentialGene/trassembly.html

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

The mmgenome R package also facilitates effortless integration with additional data sources and hence should not be seen as "yet another binning method", but rather a package to integrate different binning strategies.

All functions in the mmgenome R package has associated documentation, check it out in R by e.g. ?mmplot.

Address of the bookmark: https://github.com/MadsAlbertsen/mmgenome

August1-2, 2014

Organised By

Centre of Excellence in Bioinformatics
Bioinformatics Infrastructure Facility
Department of Biochemistry
University of Lucknow
Lucknow-226007

Course Contents

Molecular Modeling
Homology Modeling
Molecular Docking
Post-structural Analyses

Molecular Dynamics (MD)
Simulation
Linux Introduction
Gromacs Installation

MD Simulation of Protein ligand complex
Analyses of MD
Trajectories
Visualization of Dynamic
complexes

Important Dates

Registration Begins June 25, 2014
Registration Closes July 25, 2014

Brochure : www.lkouniv.ac.in/conference/Brochure_August,%202014.pdf

CPAT - https://github.com/liguowang/cpat, http://lilab.research.bcm.edu/ (web server)

CPC2 - https://github.com/gao-lab/CPC2_standalone, http://cpc2.gao-lab.org/ (web server)

Infernal - http://eddylab.org/infernal/, https://github.com/EddyRivasLab/infernal

NCBI RefSeq - https://www.ncbi.nlm.nih.gov/refseq/

Rfam - http://rfam.xfam.org/, https://docs.rfam.org/en/latest/index.html

SILVA - https://www.arb-silva.de/

RNAmmer - http://www.cbs.dtu.dk/services/RNAmmer/ (web server, standalone download link)

Reference

Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014) Orione, a web-based framework for NGS analysis in microbiology. Bioinformatics [Epub ahead of print]. [article]

Address of the bookmark: http://orione.crs4.it/

Metagenomic simulators are a way of dealing with this issue; they produce artificial sequencing datasets in which both the composition of the microorganisms and the expected results are known. Researchers can then test a pipeline on such a controlled dataset and see precisely how well it functions. However, as metagenomic studies have become larger, more diverse, and more complex, simply generating artificial reads is no longer sufficient. The simulators now have to replicate community variation, sequencing errors, different sequencing platforms, massive data volumes, and even laboratory-based biases.

MIDASim (2024) was designed with a particular emphasis on microbial community dynamics. Unlike previous tools which regarded each simulated sample as individual, MIDASim is able to efficiently generate realistic multi-sample microbiome datasets, covering the cases where changes over time are being tracked. This feature is particularly useful when investigating how microbial communities vary between people, different experimental setups, or at various times. By overcoming the computational limitations of earlier simulation methods, MIDASim has made it easier to carry out large and complex microbiome simulation studies.

A major challenge is scale. Since modern metagenomic projects can involve millions or even billions of sequencing reads, generating such large synthetic datasets can become a significant computational problem. In order to tackle this issue, Izzy (2025) was developed. As a high-throughput metagenomic read simulator, Izzy is designed with speed in mind while still reproducing the typical patterns of sequencing errors. The people who developed it stated that it can be as much as 60 times faster than earlier tools, which makes large-scale simulations far more practical. Now, researchers can begin to ask not how long a simulation will take but rather what size dataset they want to test.

At the same time, the sequencing technology became more diverse. Researchers were not any longer restricted to using Illumina short reads since Oxford Nanopore and PacBio long-read sequencing also became important in the field of metagenomic research. In order to address these new requirements, MeSS (Metagenomic Sequence Simulator, 2025) was developed. The programme is based on a Snakemake framework and is therefore compatible with the Illumina, Oxford Nanopore and PacBio platforms. MeSS has also been designed to operate efficiently and to use less memory than earlier tools; it provides ready-made templates for the microbiomes of different sites in the human body, thus giving researchers a simple means of beginning to generate realistic simulated communities.

The simulation problem became even more specialized. Suppose that sequencing is not based on uniform sampling of DNA? In the case of targeted sequencing and hybridization-capture experiments, particular sequences are deliberately enriched, and the probability of capturing a target depends on both the probe and the target sequence. A standard model based on uniform sampling might fail to take into account this important feature of real experiments. That is why RAmpSim has been designed specifically to handle simulations for capture-based and targeted sequencing. Instead of depending solely on the assumption of uniform sampling, it includes a thermodynamic nearest-neighbor energy model to represent probe–target interactions and sequence-dependent enrichment. As a result, it is especially suitable for applications such as hybridization capture in metagenomics, where the efficiency of recovering a sequence can depend strongly on how it relates to the capture probes. RAmpSim thus reflects a wider trend towards simulations that aim not only to reproduce sequencing output but also key aspects of the experimental process itself.

MIDASim, Izzy, MeSS and RAmpSim all demonstrate how fast metagenomic simulation is evolving. MIDASim is able to deal with realistic variation within a large number of and changing microbial communities. Izzy overcomes the problem of generating very large datasets. MeSS provides support for a number of sequencing technologies in an efficient and repeatable manner. RAmpSim increases the experimental realism for capture-based sequencing. Although each of these tools addresses a separate issue, they all contribute to advancing the field.

Modern metagenomic simulation therefore aims not only at producing artificial FASTQ files. Researchers nowadays desire simulated datasets that reflect community changes, incorporate the characteristics of the sequencing platform, include real error patterns, take into account computational scale, and account for experimental biases. As metagenomic workflows become more advanced, the simulators have to keep up with this development. The tools are now going beyond that of simple data generators; they produce controlled versions of complex metagenomic experiments and thus provide researchers with something that ordinary sequencing data seldom does: a dataset in which the answer is known before any analysis takes place.

Most people who have a balanced translocation have the right amount of chromosome material but it has been rearranged in some way. This may happen if two chromosomes swap pieces (a reciprocal translocation). In other cases two whole chromosomes may become stuck together (a Robertsonian translocation). This page describes what happens when someone has a reciprocal translocation.

Reciprocal chromosomal translocations occur following double-strand breaks (DSBs) in DNA when a section of one chromosome is exchanged with that of another, non-homologous chromosome. These exchanges may produce a dysfunctional fusion gene that disrupts cell growth and survival pathways, such as the translocations seen in leukemia and childhood sarcomas.

Chromosomal translocations have been well studied in cancer cell lines which are associated with two types of cancer, acute myeloid leukemia and Ewing's sarcoma, but determining how they contribute to cancer development is complicated by additional mutations and altered gene expression profiles in these cultured cells. Now, Juan Carlos Ramirez, head of the Viral Vector Facility at the Fundacion Centro Nacional de Investigaciones Cardiovasculares (CNIC) and his colleagues Raul Torres at CNIC and Sandra Rodriguez-Peralez at the Spanish National Cancer Center (CNIO) in Madrid, Spain have used a new genome editing tool, CRISPR-Cas9, to induce chromosomal translocations for the first time in a human cell line and in primary cells. The study's authors conclude by stating that the use of this technology will allow for the clarification of how and why chromosomal translocation occurs, which without doubt will allow new anti-cancer therapeutic strategies to be tackled.

Using RNA-Guided Endonuclease (RGEN) technology or CRISPR/Cas9 genome engineering technology, CNIO and CNIC researchers have shown that it is possible to obtain such chromosomal translocations. The CRISPR-Cas9 system is extremely simple to introduce a cut at the desired locus, easier to design, and cheaper than many other systems. Using the CRISPR-Cas9 system, Ramirez and his colleagues reproduced the translocations observed in Ewing’s Sarcoma (ES) and Acute Myeloid Leukemia (AML) patient cell lines in HEK293 cells and also generated the ES translocation in human mesenchymal stem cells and the AML translocation in umbilical cord blood cells.

By focusing on chromosomal translocation without the confounding characteristics of established cell lines, these new cells lines should help answer the fundamental question of what causes a cell to become cancerous. Ramirez and his team now look forward to modeling other chromosome translocations in a variety of cell types.

Reference:

http://en.wikipedia.org/wiki/Chromosomal_translocation

http://www.nature.com/ncomms/2014/140603/ncomms4964/abs/ncomms4964.html

However, nanopore sequencing is an emerging and rapidly developing technology. It is not clear what will be most informative. We hope that IONiseR will provide a framework for visualisation of metrics that we haven’t thought of, and welcome feedback at mike.smith@embl.de.

If you’re not interested in the quality assement of the raw or event level data, and want to jump straight to the getting FASTQ format files from fast5 files you can go straight to the final section of this document.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/IONiseR/inst/doc/IONiseR.html

Brief description: We are looking for suitable candidates in the area o computational biology/bioinformatics/genomics or related field for next-generation sequencing (NGS) data analysis for small-RNAs, RNA-Seq and targeted resequencing of plants and associated organisms. We are an interdisciplinary group where projects equally involve bioinformatics and systems biology (specially microarrays and next-generation sequencing (NGS) data analysis and its use), along with plant molecular biology, genetic engineering, field biology, and analytical plant chemistry for understanding response of plants to biotic stresses.

Essential qualification: MSc/BTech/MTech/PhD (or other suitable qualification) in disciplines preferable to bioinformatics, computational biology, computer application (or equivalent)/ ‘Advance Post-Graduate Diploma in Bioinformatics’. Proficiency in programming languages (such as Perl, C++) and/or statistics (proficient in R for example) is compulsory.

Desirable qualification: Experience in the field of genomics e.g. microarray analysis, NGS, genome annotation, database development and management, software development, systems and network biology (or related fields) will be preferred.

Application process: Applications should contain CV along with brief description (maximum 1 page) of research conducted (highlighting skills and experience) till now. Applications should be sent by e-mail to Shree Prakash Pandey, Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur Campus, WB, India within 14 days of this advertisement.

E-mail: sppiiserkol@gmail.com, sppandey@iiserkol.ac.in

Advertisement:

http://www.iiserkol.ac.in/announcements/adverts/671-advt_ra_shree_prakash_july_2014