BOL: Related items

SERB Sponsored School in Evolutionary Biology 2019, 12–26 March 2019

Fri, 14 Dec 2018 18:25:07 -0600

Venue: Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR)
Jakkur,
Bengaluru 560 064

Evolution provides the overarching framework for organizing our understanding of biological phenomena at multiple structural levels and spatio-temporal scales. As such, evolutionary biology is more of a perspective or weltanschauung on the living world rather than a sub-discipline of biology. Unfortunately, despite the centrality of evolution to all thinking in biology, evolutionary biology remains neglected in the Indian context although research in many areas, not traditionally thought to be within the ambit of evolutionary biology, would greatly benefit from an evolutionary perspective.

The first school in evolutionary biology in the country, sponsored by SERB, will be conducted at the Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru. The school aims to provide participants with an introduction to the historical development of evolutionary thought and the various ecological, genetic and developmental aspects of the evolutionary process. The school will also provide an introduction to formal evolutionary theory and statistical methods for the analysis of evolutionary data.

Master’s/Ph.D. scholars, postdoctoral researchers and young faculty members in India may apply online on or before 31 December 2018 at http://school2019.serbevolschool.in, where additional details about the school can also be found. In case of any difficulty with the submission of applications, please e-mail Prof. T. N. C. Vidya, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bengaluru, at tncvidya.lab@gmail.com.

Selected participants will be provided 3-tier AC train fare and local hospitality. There is no registration fee.

A guide to machine learning for biologists

Abhi — Tue, 28 Dec 2021 01:43:25 -0600

Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, the specific methods are highly variable and can appear baffling at first glance. In this Review, we hope to give readers a moderate introduction to a few fundamental machine learning techniques, including the most recently created and frequently used deep neural network techniques. We illustrate how different algorithms may be adapted to specific types of biological data, as well as some best practises and points to consider when embarking on machine learning studies. There is also discussion of several upcoming directions in machine learning methodology.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

phyloXML: XML for evolutionary biology and comparative genomics

Jit — Wed, 29 Nov 2017 10:04:48 -0600

phyloXML (example) is an XML language designed to describe phylogenetic trees (or networks) and associated data. PhyloXML provides elements for commonly used features, such as taxonomic information, gene names and identifiers, branch lengths, support values, and gene duplication and speciation events. Using these standardized elements allows interoperability between various applications and databases. Furthermore, both due to extensible nature of XML itself and the provision of elements by phyloXML, extensibility as well as domain specific applications are ensured. The structure of phyloXML is described by XML Schema Definition (XSD) language.

Address of the bookmark: http://www.phyloxml.org/

BioCoder : newsletter of that revolution. It’s about biology as it moves from research labs into startup incubators, hacker spaces, and even homes

Jit — Sun, 02 Feb 2020 07:43:52 -0600

BioCoder features:

Novel therapeutic discovery strategies
Hardware such as low-cost lab equipment or diagnostics
Open or low-cost bioinformatics tools
Engineered organisms for the production of small molecules, biologics, or other products
Research projects at a community labspace or projects for science education or public engagement
Hardware or software for lab automation
Citizen science or DIY research projects
Science policy
Tools to increase reproducibility in research, or anything related

Address of the bookmark: https://www.oreilly.com/biocoder/

PhD position in Molecular Cell Biology

Sat, 15 Feb 2020 06:09:55 -0600

https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/wissenschaftliche-r-mitarbeiter-in/phd-position-molecular-cell-biology-129604.html?suid=0ec057818886c1eceac674ca3f83943367a6cbe2

Essential experience / qualifications:
We are looking for highly motivated candidates holding a Master / Diploma in Biology, Biochemistry, Molecular Medicine or similar; solid knowledge of molecular and cell biological techniques; good English knowledge.

Applications:
Please send your application (including CV, letter of motivation, contact information of two references, and list of publication) by 13.03.2020 at the latest to:

Universitätsklinikum Erlangen
Chirurgische Klinik
Translational Research Center
Prof. Dr. rer. nat. Michael Stürzl
Schwabachanlage 12
91054 Erlangen
E-Mail: michael.stuerzl@uk-erlangen.de

AccNET

Jitendra Narayan — Fri, 07 Oct 2016 05:22:11 -0500

AccNET is a Perl application that presents a new way to study the accessory genome of a given set of organisms. Using the proteomes of these organisms, AccNET create a bipartite network compatible with common network analysis platforms. AccNET collects phylogenetic and functional information in a network improving the analysis capability. Networks offer a new perspective of organism organization through elements acquired by horizontal gene transfers and not constricted by hierarchical structures.

More at https://www.youtube.com/watch?v=vdGuy1GAJrQ

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

JASMINE: Jointly Accurate Sv Merging with Intersample Network Edges

Shruti Paniwala — Sat, 02 Jul 2022 11:41:53 -0500

This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.

Address of the bookmark: https://github.com/mkirsche/Jasmine

Patterns: a modeling tool dedicated to biological network modeling

Abhimanyu Singh — Fri, 26 Jul 2019 01:11:59 -0500

It is designed to work with patterned data. Famous examples of problems related to patterned data are:

recovering signals in networks after a stimulation (cascade network reverse engineering),
analysing periodic signals.

Address of the bookmark: https://github.com/fbertran/Patterns

Useful links to therapy, disease, drug and drug-target network data:

Jit — Mon, 01 Jun 2020 11:47:51 -0500

Useful links to therapy, disease, drug and drug-target network data:

DrugBank:

a bioinformatics- cheminformatics resource combining detailed drug data with comprehensive drug target information with >4900 drug (~3500 experimental) and >1500 non-redundant protein entries http://www.drugbank.ca/

Drug-Target Network:

network data of 890 drugs and 394 target human proteins http://www.nature.com/nbt/journal/v25/ n10/suppinfo/nbt1338_S1.html

Drug-Therapy Network:

three layers of drug-therapy networks according to the ATC classification http://www.biomedcentral.com/1471-2210/8/5/additional/

FDA Orange Book:

approved drug products with therapeutic equivalence evaluations http://www.fda.gov/cder/ob/HIDdb: Thomson Investigational drugs database including information on 107000 patents, 25000 investigational drugs and 80000 chemical structures http://scientific.thomson.com/products/iddb/HOMIM: a knowledgebase of human genes and genetic disorders http://www.ncbi.nlm.nih.gov/ sites/entrez?db=omim

PDTD:

3D drug target structure database with a target identification option http://www.dddc.ac.cn/pdtd/

Predicted drug targets:

a set of 1383 predicted drug targets http://www.biomedcentral.com/1471-2105/8/353/additional/ [25] Protein ligand network: a network of 4208 ligands and ~15000 binding sites http://pbil.kaist.ac.kr/~parkkw/Lnet/

TDR Targets Database:

identification and ranking targets against neglected tropical diseases http://tdrtargets.org/

Therapeutic Target Database:

lists >1500 therapeutic targets, disease conditions and corresponding drugs http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp

Bioinformatics software for biologists in the genomics era

Poonam Mahapatra — Sun, 22 Dec 2013 17:31:05 -0600

The genome sequencing revolution is approaching a landmark figure of 1000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale datasets.

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full