BOL: Related items

Nucleus: Python and C++ code for reading and writing genomics data.

Jit — Sun, 02 Feb 2020 08:14:19 -0600

Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF. In addition, Nucleus enables painless integration with the TensorFlow machine learning framework, as anywhere a genomics file is consumed or produced, a TensorFlow tfrecords file may be used instead.

Address of the bookmark: https://github.com/google/nucleus

Fully funded position as PhD Research Fellow in genomics/bioinformatics

Wed, 03 Feb 2021 04:18:57 -0600

A fully funded position as PhD Research Fellow in genomics/bioinformatics is available at the Section for Genetics and Evolutionary Biology (EVOGENE) at the Department of Biosciences, University of Oslo.

The fellowship will be for a period of 3 years, or for a period of 4 years, with 25 % compulsory work (e.g. teaching responsibilities at the department) contingent on the qualifications of the candidate and the teaching needs of the department.

Starting date no later than October 1, 2021.

More at https://www.jobbnorge.no/en/available-jobs/job/199984/phd-research-fellow-in-genomics-and-bioinformatics

CoronaVIR: Computational Resources on Novel Coronavirus (SARS-CoV-2 or COVID-19)

LEGE — Tue, 27 Apr 2021 01:58:36 -0500

Aim of this web site is to facilitate the scientific community to fight against severe pandemic disease COVID-19 caused by SARS-CoV-2. Here, We have collected and organized information related to novel strain of coronavirus, i.e. SARS-CoV-2.and its resulting disease COVID-19 from the literature and other resources from the Internet. We are providing links to appropriate literature. Moreover, we are Bioinformatics Group, based on our knowledge and expertise, we are also proposing potential diagnostics primers, peptide and RNA based vaccine candidates and potential drug molecules. These are predicted candidates, need to be validated by experimental Researchers, who have appropriate infrastructure. It is an integrated multi-omics repository dedicated to current genomic, proteomic, diagnostic and therapeutic knowledge about coronaviruses particularly the recent strain, i.e. SARS-CoV-2 or 2019-nCoV. This web resource will be helpful for the researchers engaged in the development of therapies and drugs for the COVID-19. The information is collected from various available resources.
Cite: Patiyal, Sumeet, et al. “A Web-based Platform on COVID-19 to Maintain Predicted Diagnostic, Drug
and Vaccine Candidates.” Monoclon Antib Immunodiagn Immunother. doi.org/10.1089/mab.2020.0035

Address of the bookmark: https://webs.iiitd.edu.in/raghava/coronavir/

scikit-bio™ is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics.

Jit — Fri, 02 Mar 2018 04:29:47 -0600

scikit-bio is currently in beta. We are very actively developing it, and backward-incompatible interface changes can and will arise. To avoid these types of changes being a surprise to our users, our public APIs are decorated to make it clear to users when an API can be relied upon (stable) and when it may be subject to change (experimental). See the API stability docs for more details, including what we mean by stable and experimental in this context.

Address of the bookmark: http://scikit-bio.org/

Online resources on must-read papers in evolutionary biology, for a literature club

Shruti Paniwala — Tue, 28 Jun 2022 07:29:08 -0500

1.       *Nick Barton:*

- The textbook "Evolution" by Nick Barton, with resources for
  exploring the literature: Barton, N. H., Briggs, D. E. G., Eisen, J.
  A., Goldstein, D. B., & Patel, N. H. (2007). Evolution. Cold Spring
  Harbor Laboratory Press.

- Papers from a course named "Classics in Evolutionary Biology":

Evolutionary Synthesis
1. Haldane, J. B. S. 1932. The causes of evolution. Longmans. New York.
   (esp. Ch. IV).
2. Fisher, R. A. 1930. The genetical theory of natural selection. Oxford
   University Press, Oxford. Selected Sections - Fundamental Theorem.

Genetic Variation
1a. Lewontin, R. C., and J. L. Hubby. 1966. A molecular approach to
the study of genic heterozygosity in natural populations. II. Amount
of variation and degree of heterozygosity in natural populations of
Drosophila pseudoobscura. Genetics. 54:595-609.

1b. Sachidandam et al. 2001. A map of human genome sequence variation
containing 1.42 million single nucleotide polymorphisms. 409: 928-33.

2. Wright S., Dobzhansky T., Hovanitz W. 1942 Genetics of natural
populations VII The allelism of lethals in the third chromosome of
Drosophila pseudoobscura. Genetics 27: 363-394.

Recombination and evolution
1. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits
to artificial selection. Genet. Res. 8:269-294.

2. Maynard Smith and Haigh. 1974. The hitch-hiking effect of a favourable
gene. Genet. Res. 23: 23-35.

Understanding sequence variation
1. Begun D. J., Aquadro C. F., 1992 Levels of naturally occurring DNA
polymorphism correlate with recombination rate in Drosophila melanogaster.
Nature 356: 519-520.

2. Green R. E., Reich D., Pääbo S., 2010 A draft sequence of the
Neandertal genome. Science 328: 710-722.

Quantitative Genetics:  variation in complex traits
1. Galton F., 1877 Typical laws of heredity. Nature 15: 492-495-
512-514- 532-533.

2. Turelli M., 1984 Heritable genetic variation via
mutation-selection balance: Lerch's Zeta meets the abdominal
bristle. Theor. Popul. Biol. 25: 138-193.

Quantitative Genetics:  finding the genes
1. Shrimpton A. E., Robertson A., 1988 The Isolation of polygenic factors
controlling bristle score in Drosophila melanogaster II Distribution of
third chromosome bristle effects within chromosome sections. Genetics
118: 445-459.

2. Boyle E. A., Li Y. I., Pritchard J. K., 2017 An expanded view of
complex traits: from polygenic to omnigenic. Cell 169: 1177-1186.

Neutral Evolution
1. Kimura, M. 1968. Evolutionary rate at the molecular level. Science.
217:624-626.

2a. Kern A. D., Hahn M. W., 2018 The Neutral Theory in Light of Natural
Selection. Molecular Biology and Evolution 110: 21077-6.

2b. Jensen J. D., Payseur B. A., Stephan W., Aquadro C. F., Lynch M.,
Charlesworth D., Charlesworth B., 2018 The importance of the Neutral Theory
in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 112:
2109-4.

2c. Ellegren & Galtier. 2016. Determinants of genetic diversity. Nature
Reviews Genetics.

Mutation and Genetic Variability
1. Luria, S. E., and M. Delbrück. 1943. Mutations of Bacteria from Virus
Sensitivity to Virus Resistance. Genetics. 28(6):491-511.

2. Hill, W G. 1982. "Rates of Change in Quantitative Traits From Fixation
of New Mutations." Proceedings of the National Academy of Sciences (U.S.A.)
79: 142-45.

Testing for selection
1. McDonald & Kreitman. 1991. Adaptive protein evolution at the Adh locus
in Drosophila. Nature.

2. Begun, et al. Mol. Biol. Evol. 16, 1816-1819 (1999).

3. Siddiq et al. 2016. Experimental test and refutation of a classic case
of molecular adaptation in Drosophila melanogaster.  Nature Ecology &
Evolution.

The shifting balance
1. Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and
selection in evolution. Proceedings of the VI International Congress of
Genetics: 1. pp 356-366.

2. Coyne, J.A., N.H. Barton, and M. Turelli. 1997. A critique of Wright's
shifting balance theory of evolution.  Evolution 51: 643-671.

3. Barton. 2016. Sewall Wright on Evolution in Mendelian Populations and
the "Shifting Balance". Genetics.

Evolution of Sex
1.  Muller, H.J. 1964. The relation of recombination to mutational advance.
Mutation Res. 1(1):2-9

2. McDonald et al. 2016. Sex speeds adaptation by altering the dynamics of
molecular evolution. Nature.

Kin Selection, Cooperation, and Conflict
1. Hamilton, W. D. 1964. The genetical evolution of social behaviour I.
Journal of Theoretical Biology. 7:1-52.

2. Trivers, R. L. 1974 Parent-offspring conflict. American Zoologist.
14(1):249-264.

Sexual Selection
1. Zahavi, A. 1975. Mate selection - a selection of a handicap. J. Theor.
Biol. 53:205-214.

2. Kirkpatrick, M., and Ryan, M.J. 1991. The evolution of mating
preferences and the paradox of the lek. Nature. 350:33-38.

Fitness Landscapes
1. Dean, A. 1995. A Molecular Investigation of Genotype by Environment
Interactions. Genetics. 139:19-33.

2. Costanzo et al. 2010. The Genetic Landscape of a Cell. Science.

Speciation
1. Coyne, J. A., and H. A. Orr. 1989. Patterns of speciation in Drosophila.
Evolution. 43:362-381.

2. Corbett-Detig et al. 2013. Genetic incompatibilities are widespread
within species. Nature.

2.       *Marcos Antezana:*

Valen, L. v. 1975. Energy and Evolution. University of Chicago, Department
of Biology.

3.       *Remco Folkertsma:*

1. The work by Hopi Hoekstra on local adaptation and oldfield mice

2. Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Müller,
I., ... & Wolf, J. B. (2014). The genomic landscape underlying phenotypic
integrity in the face of gene flow in crows. Science, 344(6190), 1410-1414.

4.       *Joshka Kaufmann and Leslie Turner*

They offer us a link to 'papers every evolutionary biologist should read',
the papers are collected by Leslie Turner.
https://static1.squarespace.com/static/53e8cb7ce4b02c4bc3aeeee4/t/5ab8fcb670a6ad55c67fcdf4/1522072758665/EvoBioClassicsRefList.pdf

5.       *Sarah Stockwell*

Matt Ridley collected classic papers in evolutionary biology and printed
part of these papers in his book Evolution (see Matt Ridley. Evolution
(Univ. of Oxford Press, 2nd edition, 2004))

What Junk DNA? It’s an Operating System

Rahul Agarwal — Mon, 19 Aug 2013 15:24:26 -0500

The report adds to growing experimental support for the idea that all that extra stuff in the human genes, once referred to as “junk DNA,” is more than functionless, space-filling material that happens to make up nearly 98% of the genome. The paper adds to a growing body of knowledge establishing a considerable role for this material in the regulation of gene expression and its potential role in human disease.

Address of the bookmark: http://www.genengnews.com/keywordsandtools/print/3/32115/

Pathway Analysis

Rahul Agarwal — Fri, 03 Oct 2014 08:51:13 -0500

Pathway Analysis is usually performed with aim to enrich the genes with their functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to disclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, their transcripts, and their gene products. However, existing tools and dbs mainly based on knowledge driven approach in which pathways will be identified by finding the correlation between the information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) and gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.

Introductory Articles/ppt/sources:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375

http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf

http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html

http://davetang.org/muse/tag/pathway/

https://www.biostars.org/p/42219/

http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf

http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf

Impotant Database and Tools:

GeneMANIA, Cytoscape, IPA and Metacore (Commerical ), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go

Popular R based tools:

Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat

More:

http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+

bpRNA: large-scale automated annotation and analysis of RNA secondary structure

Rahul Nayak — Wed, 23 May 2018 03:24:33 -0500

bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature.

The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.

Address of the bookmark: http://github.com/hendrixlab/bpRNA

HDOCK SERVER

Neel — Tue, 16 Jun 2020 01:54:41 -0500

HDOCK SERVER

Protein-protein and protein-DNA/RNA docking based on a hybrid algorithm of template-based modeling and ab initio free docking.

The HDOCK server distinguishes itself from similar docking servers in its ability to support amino acid sequences as input and a hybrid docking strategy in which experimental information about the protein–protein binding site and small-angle X-ray scattering can be incorporated during the docking and post-docking processes.

Address of the bookmark: http://hdock.phys.hust.edu.cn/

Exploring RNA Sequence Analysis: Tools for Every Bioinformatician

Neel — Fri, 13 Dec 2024 04:03:04 -0600

RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

FASTQC: For quality control of raw RNA-seq reads.
Trimmomatic: For trimming and filtering RNA-seq reads.
HISAT2/STAR: High-performance aligners for RNA-seq reads.
FeatureCounts: For quantifying gene expression.
DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

Trinity: For de novo transcriptome assembly.
StringTie: For transcript assembly and quantification from RNA-seq alignments.
TransDecoder: To predict coding regions within assembled transcripts.
TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

Infernal: For identifying ncRNA sequences based on covariance models.
Rfam: Database and tools for ncRNA families.
miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
RNAstructure: Tools for RNA secondary structure prediction and analysis.
MEME Suite: For identifying motifs in RNA sequences.
IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

REDItools: For RNA editing analysis.
m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

FLAIR: For isoform-level analysis of long-read RNA-seq data.
NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

RBPmap: For identifying RNA-binding protein motifs.
PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

IGV: Genome browser for visualizing RNA-seq alignments.
Circos: Circular visualization of RNA-seq data.
DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.