BOL: Related items

Professor (all levels) in Bioinformatics and Computational Biology

Tue, 22 Nov 2016 05:43:38 -0600

King Abdullah University of Science and Technology (KAUST) (kaust.edu.sa) is seeking a highly motivated and skilled faculty member for the Bioinformatics track whose research focuses on development of methods and tools for Bioinformatics and Computational Biology.
KAUST is an international, graduate-level research university dedicated to advancing science and technology through interdisciplinary research, education, and innovation. Located on the shores of the Red Sea in Saudi Arabia, KAUST offers superb research facilities, generous assured research funding, and internationally competitive salaries, attracting top international faculty, scientists, engineers, and students to conduct fundamental and goal-oriented research to address the world’s pressing scientific and technological challenges in the areas of food, water, energy, and the environment.
The successful applicant is expected to develop world-leading research in domain of bioinformatics/computational biology with focus on development of novel computational approaches for efficient and accurate methods of analyzing biological phenomena at molecular level. The faculty member will be part of the Computational Bioscience Research Center (CBRC) within the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. The position will remain open until filled.

Requirements:

PhD or equivalent in a Computer Science, Mathematics or Engineering discipline. Candidates should be well-established within the research field relevant to the position grade. They should demonstrate original research and experience at the highest international level.

Responsibilities and tasks:

Research competence in the following areas is preferred:
Analysis of next generation sequencing (NGS) and other ‘omics’ data (e.g. CAGE, ChIP-Seq, DHS, RNA-Seq, Ribo-Seq, proteomic, metabolic and NMR spectra, etc.).
Signaling, regulatory and metabolic pathways analysis.
Development of tools (web-based and standalone) suited for efficient computational biology/bioinformatics.

Visit cemse.kaust.edu.sa to apply.

EXCAVATOR2tool

Bulbul — Wed, 30 Nov 2016 04:09:19 -0600

EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.
EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it’s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.

Address of the bookmark: https://sourceforge.net/projects/excavator2tool/

SGA: String Graph Assembler

Jit — Thu, 08 Dec 2016 05:08:59 -0600

SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.

More at

https://github.com/jts/sga

SGA dependencies:
-google sparse hash library (http://code.google.com/p/google-sparsehash/)
-the bamtools library (https://github.com/pezmaster31/bamtools)
-zlib (http://www.zlib.net/)
-(optional but suggested) the jemalloc memory allocator (http://www.canonware.com/jemalloc/download.html)

Address of the bookmark: https://github.com/jts/sga

Understanding Greedy Algorithms

Jit — Mon, 12 Dec 2016 04:37:40 -0600

Learning greedy algo for biologist.

https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/

This webpage is also useful for the same:

http://learninglover.com/examples.php?id=59

http://www.cs.rpi.edu/~magdon/ps/conference/super_biokdd.pdf

https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/lecture-slides/MIT7_91JS14_Lecture6.pdf

http://schatzlab.cshl.edu/teaching/AssemblyClass/01.%20Assembly%20Intro.pdf

http://lsl.sinica.edu.tw/Services/Class/files/20150612449.pdf

http://www.cs.jhu.edu/~langmea/resources/lecture_notes/assembly_scs.pdf

https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-43.pdf

Address of the bookmark: https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/

LAST

Bulbul — Mon, 19 Dec 2016 14:07:53 -0600

LAST can:

Handle big sequence data, e.g:
- Compare two vertebrate genomes
- Align billions of DNA reads to a genome
Indicate the reliability of each aligned column.
Use sequence quality data properly.
Compare DNA to proteins, with frameshifts.
Compare PSSMs to sequences
Calculate the likelihood of chance similarities between random sequences.
Do split and spliced alignment.
Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

Mauve: a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion

Jit — Sat, 24 Dec 2016 09:20:53 -0600

Mauve is a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion. Multiple genome alignments provide a basis for research into comparative genomics and the study of genome-wide evolutionary dynamics.

Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the lengths of sequences being aligned. For example, a pair of Y. pestis genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours. However, the current algorithm’s compute time (progressiveMauve) scales cubically in the number of genomes to align, making it unsuitable for datasets containing more than 50-100 bacterial genomes.

Address of the bookmark: http://darlinglab.org/mauve/mauve.html

Finding Patterns in Biological Sequences

Jit — Thu, 22 Dec 2016 10:30:49 -0600

In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/∼tvinar/cs798g/motif.

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

FSA: Fast Statistical Alignment

Jit — Mon, 06 Feb 2017 04:26:01 -0600

FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in "Posterior Decoding Methods for Optimization and Control of Multiple Alignments."

FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:

FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.
FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.
FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.
The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and movie at the top of the page).

You can see more information on the FAQ.

Address of the bookmark: http://fsa.sourceforge.net/

Laj: viewing and manipulating the output from pairwise alignment programs

Abhimanyu Singh — Wed, 01 Mar 2017 08:35:40 -0600

Laj is a tool for viewing and manipulating the output from pairwise alignment programs such as blastz. It can display interactive dotplot, pip, and text representations of the alignments, a diagram showing the locations of exons and repeats, and annotation links to other web sites containing additional information about particular regions.

The program is written in Java in order to provide a graphical user interface that is portable across a variety of computer platforms; indeed its name stands for "Local Alignments with Java". Currently it exists in two forms, a stand-alone application and a web-based applet, with slightly different capabilities.

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

PANDASEQ

Shruti Paniwala — Mon, 23 Jan 2017 04:54:32 -0600

PANDASEQ assembles paired-end Illumina reads into sequences, trying to correct for errors and uncalled bases. The assembler reads two files in FASTQ format with quality information. If amplification primers were used (e.g., to isolate a variable region of the 16S gene, or the constant regions around zinc finger binding residues), they can be removed from the sequence during assembly. The final sequence will correct any uncalled bases in the overlapping region using the complementary strand. When mismatches occur in the overlapping region, the base with the better quality score is chosen.
The algorithm is as follows:

1.Find the positions where the forward and reverse primers match best above the threshold and discard the ends of the sequence, including the primer.
2.Pick and overlap to maximise the probability of the forward and reverse reads having come from a single piece of DNA.
3.Identify the masking of the end of the read with the quality score B or # as done by CASAVA and adjust the probabilities in this region.
4.Construct an assembled sequence between the primers and calculate the quality.
5.Check for various constraints, including quality, length, uncalled bases, and user-supplied modules.

http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html

Address of the bookmark: http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html