BOL: Related items

biostarhandbook

Neel — Fri, 27 Aug 2021 01:31:01 -0500

Nice book collection for bioinformatician ... highly recommended.

Address of the bookmark: https://www.biostarhandbook.com/

“On” and “Off” the neuron !!!

Rahul Nayak — Fri, 25 Apr 2014 19:31:13 -0500

Optogenetics is a recent innovation in neuroscience that gives researchers the ability to control the activity of neurons with light. With this powerful tool, researchers are teasing apart the biological basis of memory, behavior, and disease (see “Scientists Make Mice ‘Remember’ Things That Didn’t Happen” and “An On-Off Switch for Anxiety,”). But for the first several years of this technology’s existence, the proteins that scientists added to neurons to make them react to light were only good at activating neurons. That limited researchers’ ability to understand neuronal circuits, sets of interconnected neurons that are thought to control behavior and, when misfiring, to underlie many brain conditions. Problems can arise from any imbalance in circuit activity, whether too much or too little.

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.

Check Linux server configuration !!

Rahul Nayak — Tue, 06 May 2014 01:10:57 -0500

Bioinformatician uses servers for computational analysis. Sometime we need to check the server details before running our programs or tools. Here I am showing some basic commands using them you can gather the system/server information.

To check what version of Operating System is installed on the server you can use the following commands:-
=================================================================
1.cat /etc/issue
[root@localhost ~]# cat /etc/issue
Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Kernel \r on an \m

2.cat /etc/redhat-release
[root@localhost ~]# cat /etc/redhat-release
Red Hat Enterprise Linux Server release 5.5 (Tikanga)

3.lsb_release -a
[root@localhost ~]# lsb_release -a
LSB Version:    :core-3.1-ia32:core-3.1-noarch:graphics-3.1-ia32:graphics-3.1-noarch
Distributor ID: RedHatEnterpriseServer
Description:    Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Release:        5.5
Codename:       Tikanga

To check whether the operating system is 32 or 64bit:-
================================
# uname -i
[root@localhost ~]# uname -i
i386
(i386 represents that server is having 32bit operating system)

[root@localhost ~]# uname -i
x86_64
(x86_64 represents that server is having 64bit operating system)

To see the processor/CPU information:-
=============================
# cat /proc/cpuinfo
[root@localhost ~] cat /proc/cpuinfo
processor       : 0
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) CPU            5130 @ 2.00GHz
stepping        : 6
cpu MHz         : 1995.087
cache size      : 4096 KB
physical id     : 0
siblings        : 2
core id         : 0
cpu cores       : 2
apicid          : 0
fdiv_bug        : no
hlt_bug         : no
f00f_bug        : no
coma_bug        : no
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe nx lm constant_tsc pni monitor ds_cpl vmx tm2 ssse3 cx16 xtpr lahf_lm
bogomips        : 3990.17
(Here processor number 0 indicates that the system is having one process(processor number starts with zero))

To check memory information:-
===========================
# free -m
[root@localhost ~]# free -m
             total       used       free     shared    buffers     cached
Mem:          5066       3513       1552          0        612       2319
-/+ buffers/cache:        582       4484
Swap:         1983          0       1983

# cat /proc/meminfo
[root@localhost ~]# cat /proc/meminfo
MemTotal:      5187752 kB
MemFree:       1639300 kB
Buffers:        627024 kB
Cached:        2374944 kB
SwapCached:          0 kB
Active:        2458788 kB
Inactive:       920964 kB
HighTotal:     4325164 kB
HighFree:      1561936 kB
LowTotal:       862588 kB
LowFree:         77364 kB
SwapTotal:     2031608 kB
SwapFree:      2031608 kB
Dirty:             704 kB
Writeback:           0 kB
AnonPages:      377892 kB
Mapped:          35328 kB
Slab:           153036 kB
PageTables:       6316 kB
NFS_Unstable:        0 kB
Bounce:              0 kB
CommitLimit:   4625484 kB
Committed_AS:   977132 kB
VmallocTotal:   116728 kB
VmallocUsed:      4492 kB
VmallocChunk:   112124 kB
HugePages_Total:     0
HugePages_Free:      0
HugePages_Rsvd:      0
Hugepagesize:     2048 kB

To check the model and serial name of the server:-
=======================================
[root@localhost ~]# dmidecode | egrep -i "product name|Serial number"
Product Name: PowerEdge R710
Serial Number: AB8CDE1


To check the host name:-
=====================
[root@localhost ~]# uname -n
localhost

[root@localhost ~]# hostname
localhost

To check the kernel version:-
========================
[root@localhost ~]# uname -r
2.6.18-238.9.1.el5PAE

Data Visualization in Bioinformatics: Useful and Eye-Catching Plots for Data Analysis

LEGE — Sat, 14 Dec 2024 12:41:53 -0600

Data visualization is a cornerstone of bioinformatics, enabling researchers to interpret complex datasets effectively. With a plethora of data types—genomic sequences, expression profiles, protein interactions, and more—the right visualizations can make or break an analysis. This blog highlights some of the most useful and visually compelling plots for bioinformatics data analysis, along with tools to create them.

1. Heatmaps: Exploring Patterns in High-Dimensional Data

Heatmaps are a go-to visualization for representing high-dimensional datasets, such as gene expression or metabolomics data. They use color gradients to display data intensity, making patterns and clusters easily detectable.

Applications: Gene expression analysis, pathway enrichment, methylation studies.
Tools: Seaborn (Python), ComplexHeatmap (R), Morpheus (web-based).

Tip: Add dendrograms to visualize clustering of rows and columns for hierarchical relationships.

2. Volcano Plots: Highlighting Differential Features

Volcano plots are indispensable for identifying significantly differentially expressed genes or proteins. They plot the log2 fold change against –log10(p-value), making it easy to spot statistically significant changes.

Applications: RNA-seq, proteomics, and metabolomics.
Tools: ggplot2 (R), EnhancedVolcano (R), Plotly (Python).

Tip: Use color to highlight significant features and label key genes or proteins.

3. PCA Plots: Reducing Complexity with Principal Component Analysis

Principal Component Analysis (PCA) plots are used to reduce dimensionality and uncover trends or clusters in data. They provide insights into sample variability and grouping.

Applications: Transcriptomics, metabolomics, microbiome studies.
Tools: scikit-learn + Matplotlib (Python), prcomp (R), ClustVis (web-based).

Tip: Annotate clusters with metadata to enhance interpretability.

4. Manhattan Plots: Genome-Wide Association Studies

Manhattan plots visualize p-values across the genome, making it easy to identify significant associations in genome-wide studies. They resemble city skylines, with the highest peaks indicating loci of interest.

Applications: GWAS, QTL mapping.
Tools: qqman (R), Matplotlib (Python).

Tip: Use alternating colors for chromosomes and highlight significant SNPs for clarity.

5. Circular Plots (Circos): Visualizing Genomic Relationships

Circular plots are ideal for visualizing relationships across the genome, such as structural variations, gene duplications, or synteny.

Applications: Comparative genomics, structural variation studies.
Tools: Circos (standalone), Rcircos (R), pyCircos (Python).

Tip: Keep the plot clean and avoid overcrowding to maintain readability.

6. Sankey Diagrams: Tracking Data Flows

Sankey diagrams visualize flows or relationships between categories, often used to track changes in gene expression or pathway enrichment across conditions.

Applications: Pathway analysis, gene set enrichment analysis.
Tools: Plotly (Python), networkD3 (R).

Tip: Use gradients or distinct colors to highlight key transitions.

7. Network Graphs: Mapping Interactions

Network graphs represent relationships between entities, such as protein-protein interactions or gene regulatory networks. Nodes represent entities, and edges represent relationships.

Applications: Systems biology, interactomics.
Tools: Cytoscape (standalone), igraph (R), NetworkX (Python).

Tip: Use edge thickness or node size to represent interaction strength or centrality.

8. Violin Plots: Visualizing Data Distribution

Violin plots combine a boxplot with a density plot, showing the distribution and variability of data.

Applications: Single-cell RNA-seq, quantitative trait analysis.
Tools: Seaborn (Python), ggplot2 (R).

Tip: Split violins by groups for side-by-side comparisons.

9. Time-Series Plots: Monitoring Changes Over Time

Time-series plots display changes in variables across time points, useful for tracking gene expression dynamics or metabolic fluxes.

Applications: Time-course experiments, cell cycle studies.
Tools: Matplotlib (Python), ggplot2 (R).

Tip: Smooth the data to highlight trends while avoiding overfitting.

10. Genome Tracks: Visualizing Genomic Features

Genome tracks display multiple layers of genomic data, such as gene annotations, sequencing coverage, and epigenetic marks.

Applications: ChIP-seq, ATAC-seq, whole-genome sequencing.
Tools: IGV (standalone), pyGenomeTracks (Python).

Tip: Stack related tracks for direct comparisons.

11. UpSet Plots: Visualizing Set Intersections

UpSet plots are a powerful alternative to Venn diagrams for visualizing intersections between multiple datasets.

Applications: Overlap analysis for gene sets, pathways, or variants.
Tools: UpSetR (R), ComplexUpset (Python).

Tip: Use bar plots to represent the size of each intersection for added clarity.

12. Ridge Plots: Comparing Distributions

Ridge plots visualize the distributions of multiple datasets, stacked for easy comparison.

Applications: Transcriptomics, single-cell RNA-seq.
Tools: ggridges (R), Matplotlib (Python).

Tip: Use transparency and consistent scaling for better readability.

13. Chord Diagrams: Visualizing Connections Between Groups

Chord diagrams illustrate relationships between categories, such as shared genes between pathways or overlaps in regulatory elements.

Applications: Pathway overlap, synteny, co-expression networks.
Tools: Circlize (R), Holoviews (Python).

Tip: Use distinct colors for each group to emphasize relationships.

14. Treemaps: Hierarchical Data Representation

Treemaps visualize hierarchical data as nested rectangles, with area proportional to data size.

Applications: Ontology enrichment, pathway analysis.
Tools: Treemapify (R), Plotly (Python).

Tip: Use colors to represent additional variables, like significance or enrichment scores.

15. T-SNE/UMAP Plots: Dimensionality Reduction for Clustering

T-SNE and UMAP plots are great for visualizing high-dimensional data in two dimensions while preserving local or global structure.

Applications: Single-cell transcriptomics, clustering analyses.
Tools: scikit-learn (Python), Seurat (R).

Tip: Combine with metadata annotations for better cluster interpretation.

Bringing It All Together

The choice of visualization can significantly impact the insights gained from bioinformatics data. By selecting plots tailored to your data type and analysis goals, you can effectively communicate your findings and make your research more impactful. Whether you’re a seasoned bioinformatician or a beginner, mastering these visualizations will elevate your analyses and presentations.

Assistant Professor at Jawaharlal Nehru University in Delhi

Wed, 07 May 2014 00:29:22 -0500

Advt. No. RC/48/2014

SCHOOL OF COMPUTATIONAL AND INTEGRATIVE SCIENCES (SC&IS)

ESSENTIAL QUALIFICATION : - M.Sc./M.Tech. in Physics/ Chemistry/ Biology/ Mathematics/ Statistics/ Bioinformatics/ Computational Biology. Ph.D. in the broad areas of Bioinformatics/ Computational Biology. Candidates must have demonstrated capabilities in terms of high impact research publications in either of the above mentioned areas.

Scale of Pay : - 15600-39100/- (PB-III) AGP Rs. 6000/-

For more details on Centre/School, Specializations etc. please visit JNU website www.jnu.ac.in or contact Section Officer, Room Nos. 131-132, Recruitment Cell, Administrative Block, JNU, New Delhi – 110067, Email: recruitmentjnu2013@gmail.com The last date for the receipt of application is 15 May, 2014.

http://www.jnu.ac.in/Career/

http://www.jnu.ac.in/Career/ADVTNo_RC_48_2014.pdf
Last Apply Date:

15 May 2014

Blaxter Lab

Thu, 19 Nov 2020 08:05:28 -0600

Using these high quality genomes we explore

the evolutionary history of genes and species, building phylogenetic trees of life
the contrasting roles of horizontal gene transfer and introgression in shaping evolution
the biology of symbioses, especially symbioses between eukaryotes and bacteria, and between parasites and their hosts
the processes that drive the evolution of pattern in the structure of chromosomes
the diversity of meiofauna, particularly tardigrades, nematodes and other Ecdysozoa
the genomics of extremophilia

More at https://www.sanger.ac.uk/group/blaxter-group/

Memories Can Be Passed Down Through DNA

Sat, 10 May 2014 21:24:10 -0500

The premise of Assassin's Creed is the reliving of other people's memories stored inside DNA. Well scientists have found that in mice, it actually happens! Anthony is joined by special guest and our friend Tara Long from Hard Science to explain how this process works, and if it might apply to humans as well. Read More: Parental olfactory experience influences behavior and neural structure in subsequent generations http://www.nature.com/neuro/journal/vaop/ncurrent/abs/nn.3594.html "Using olfactory molecular specificity, we examined the inheritance of parental traumatic exposure, a phenomenon that has been frequently observed, but not understood." What Is Epigenetics? http://www.sciencemag.org/content/330/6004/611 "The cells in a multicellular organism have nominally identical DNA sequences (and therefore the same genetic instruction sets), yet maintain different terminal phenotypes. This nongenetic cellular memory, which records developmental and environmental cues (and alternative cell states in unicellular organisms), is the basis of epi-(above)-genetics." Epigenetics http://en.wikipedia.org/wiki/Epigenetics Watch More: How to Change Your Genes https://www.youtube.com/watch?v=B5DU9lgbsSE TestTube Wild Card http://testtube.com/dnews/dnews-231-how-too-many-screens-affect-our-brain?utm_source=YT&utm_medium=DNews&utm_campaign=DNWC Is Sexiness Hereditary? https://www.youtube.com/watch?v=z6STRCncvM8 ____________________ DNews is dedicated to satisfying your curiosity and to bringing you mind-bending stories & perspectives you won't find anywhere else! New videos twice daily. Watch More DNews on TestTube http://testtube.com/dnews Subscribe now! http://www.youtube.com/subscription_center?add_user=dnewschannel DNews on Twitter http://twitter.com/dnews Anthony Carboni on Twitter http://twitter.com/acarboni Laci Green on Twitter http://twitter.com/gogreen18 Trace Dominguez on Twitter http://twitter.com/trace501 DNews on Facebook http://facebook.com/dnews DNews on Google+ http://gplus.to/dnews Discovery News http://discoverynews.com

Brief Biodata Of Dr. Pranjal Chandra

Pranjal Chandra PhD — Wed, 04 Sep 2013 11:14:03 -0500

Bioinformatics JRF/SRF position at NATIONAL RESEARCH CENTRE ON PLANT BIOTECHNOLOGY

Sun, 11 May 2014 22:29:12 -0500

NATIONAL RESEARCH CENTRE ON PLANT BIOTECHNOLOGY
LBS, CENTRE, PUSA CAMPUS, IARI NEW DELHI
NEW DELHI – 110 012

WALK- IN –INTERVIEWS

Eligible candidates may appear in Walk-in-Interview on May 23, 2014 at 10 AM for the posts of Research Associates & Senior Research Fellows (SRF) in the following DST/DBT/ICAR funded projects.

1 NPTC Project on Bioinformatics and Comparative Genomics

Research Associate (One)

Rs. 24000/- + 30% HRA for masters degree holder with more than 4 years experience

Essential: Ph D in Plant Molecular Biology & Biotechnology/Genetics 0r Candidates who have already submitted their Ph D thesis in above subjects

Desirable: Research experience in Genomics, Molecular biology, Microarrays analysis, Gene cloning, transgenic Techniques , and computational analysis.

Senior Research Fellow ( UGCCSIR/ DBT/ ICAR Net qualified only): (One)

Rs. 16000/- + 30% HRA and Rs. 18000+30 HRA from 3rd year onwards

Essential:

1. ICAR/ UGCCSIR/DBT Net qualified only

2. M. Sc. (with thesis) in Biotechnology, Life Sciences, Biosciences/ Bioinformatics, Genetics/ Plant Pathology with experience in molecular biology.

Or M.Sc with more than 3 years research experiences

3. B.Sc. Agriculture or Biology

Desirable:
1. M. Sc. with thesis
2. Experience in molecular biology, plant tissue culture
3. Bioinformatics knowledge is important

2 DST JC Bose National Fellowship

Research Associate (Bioinformatics) : One

Rs.22000/- + 30% HRA for 1 & 2nd Yr., Rs. 23000+ 30% HRA for 3rd year and Rs. 24000+30% HRA for 4th &5th yr

Essential: M Ph D in Plant Molecular Biology & Biotechnology/Genetics

Desirable: Research experience in Genomics, Molecular biology, Microarrays analysis, Gene cloning, transgenic Techniques , and computational analysis.

Age limit: Max.35 years (Age relaxation of 5 years for SC/ST & women and 3 years for OBC)

The posts are purely temporary in nature and are co-terminus with the project. Initially the offer will be made for one year only and may be further extendable based on performance of the candidate. The interview will be held on May 23 , 2014 at 10:00 AM at NRCPB, LBS Building, Pusa Campus, IARI, New Delhi- 110012. The candidates must bring four copies of biodata (in the prescribed proforma), original certificates, attested photocopies of each of the certificates and an attested copy of recent passport size photograph. No. TA/DA would be given for the appearance in interview. Only the candidates having essential qualification would be entertained for the interviews. Short-listing of candidates based on academic merit and experience will be done in case of large number of applicants.

Advertisement: http://www.nrcpb.org/sites/default/files/Advertisement%20for%20RA%20and%20SRF%20Position.pdf