BOL: Related items

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

Bioinformatics tools to detect horizontal gene transfer (HGT) in genomes

Jit — Fri, 02 Mar 2018 04:56:23 -0600

Horizontal gene transfer (HGT), the “non-sexual movement of genetic material between two organisms” , is relatively common in prokaryotes and single-celled eukaryotes, but a number of factors combine to make it far rarer in multicellular eukaryotes. In order for a eukaryotic species to gain a gene by HGT, foreign DNA must enter the host nucleus, integrate into the genome, and in more complex organisms it must enter the sequestered germline in order to be transmitted to offspring. Once there, it must not experience strong negative selection, despite potential for genetic incompatibility with the host genome and mismatch between the niche of the donor and the host. Over the longer term, foreign DNA may become “domesticated” in the recipient genome and provide novel function.

Following are the popular tool to detect HGT in genomes:

T-REX / 3.22

HGT detection / download & compile

20525630

RANGER-DTL / 2.0

HGT detection / download binary

22689773

PhyloNet / 3.6.1

HGT detection / download binary

18662388

Jane / 4.01

HGT detection / download binary (!license!)

20181081

TREE-PUZZLE / 5.3.rc16

HGT detection / download & compile

11934758

CONSEL / 0.20

HGT detection / download

11751242

DarkHorse / 1.5 rev170

HGT detection / download & install

17274820

HGTector / 0.2.1

HGT detection / git clone

25159222

EGID / 1.0

HGT detection / download

22355228

GeneMarkS / 4.30

HGT detection / download binary (!license!)

9461475

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

Ligand Docking Tools and Software !

Poonam Mahapatra — Wed, 25 Apr 2018 05:05:17 -0500

Ligand docking referred to cases where small molecule (“ligand”) is being docked into much larger macromolecule ("target"). The following is partial list of docking software, focusing on free (at least for academic institutes) and/or popular docking tools.

AutoDock

Stochastic (GA)

Flexible ligand and partially flexible target

ArgusLab

Systematic

Flexible ligandX-Score based

DOCK

Systematic (IC)

Flexible ligandDOCK 3.5 (force field)

eHITS

Systematic (RBD of fragments followed by reconstruction)Flexible ligand and partially flexible targetHiTS_Score (empirical)

FlexX

Systematic (IC)Flexible ligandFlexX SF (empirical)Commercial

FLIPDock

Stochastic (GA)Flexible ligand and flexible targetAUTODOCK (empirical)

FRED

Systematic (RBD)Flexible ligandChemScore, PLP, ScreenScore, ChemGauss (empirical/consensus)

GOLD

Stochastic (GA)

Flexible ligand and partially flexible targetGoldScore, ChemScore (empirical), ASP (knowledge based)

ICM

Stochastic (MC)

Flexible ligand and partially flexible targetICM SF (empirical)

ParDOCK

Stochastic (MC)

RigidBAPPL (empirical)

PLANTS

Stochastic (ACO)Flexible ligand and partially flexible target

CHEMPLP, PLP (empirical)

Surflex

Systematic (IC/MA)Flexible ligandHammerhead based (empirical)

Point to note:

Several studies have shown that the performance of most docking tools is highly dependent on the particular characteristics of both the binding site and the ligand to be investigated, and the determination which method would be more suitable in a specific context is difficult. We encouraged you to check several docking methods to determine which one(s) work best for your system.

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

Scallop: reference-based transcriptome assembler for RNA-seq

Rahul Nayak — Tue, 08 May 2018 04:23:27 -0500

Scallop is an accurate reference-based transcript assembler. Scallop features its high accuracy in assembling multi-exon transcripts as well as lowly expressed transcripts. Scallop achieves this improvement through a novel algorithm that can be proved preserving all phasing paths from reads and paired-end reads, while also achieves both transcripts parsimony and coverage deviation minimization.

Scallop paper has been published at Nature Biotechnology. The datasets and scripts used in this paper to compare the performance of Scallop and other assemblers are available at scalloptest.

Please also checkout the podcast about Scallop (thanks Roman Cheplyaka for the interview). It is available at both the bioinformatics chat and iTunes.

https://github.com/Kingsford-Group/scallop

Address of the bookmark: https://github.com/Kingsford-Group/scallop

A History of Bioinformatics (in the Year 2039)

Wed, 23 Jul 2014 06:37:51 -0500

C. Titus Brown http://video.open-bio.org/video/1/a-history-of-bioinformatics-in-the-year-2039

Installing Bandage on Ubunty !

Jit — Tue, 08 May 2018 08:03:21 -0500

The following instructions successfully build Bandage (https://github.com/rrwick/Bandage ) on a fresh installation of Ubuntu 14.04:

Ensure the package lists are up-to-date: sudo apt-get update
Install prerequisite packages: sudo apt-get install build-essential git qtbase5-dev libqt5svg5-dev
Download the Bandage code from GitHub: git clone https://github.com/rrwick/Bandage.git
Open a terminal in the Bandage directory.
Set the environment variable to specify that you will be using Qt 5, not Qt 4: export QT_SELECT=5
Run qmake to generate a Makefile: qmake
Build the program: make
Bandage should now be an executable file.
Optionally, copy the program into /usr/local/bin: sudo make install. The Bandage build directory can then be deleted.

➜ Tools git:(master) ✗ sudo apt-get update
[sudo] password for urbe:
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➜ Tools git:(master) ✗ sudo apt-get install build-essential git qtbase5-dev libqt5svg5-dev
Reading package lists... Done
Building dependency tree
Reading state information... Done
build-essential is already the newest version (12.1ubuntu2).
git is already the newest version (1:2.7.4-0ubuntu1.3).
The following packages were automatically installed and are no longer required:
bridge-utils containerd linux-headers-4.4.0-116 linux-headers-4.4.0-116-generic linux-headers-4.4.0-21 linux-headers-4.4.0-21-generic linux-image-4.4.0-116-generic linux-image-4.4.0-21-generic
linux-image-extra-4.4.0-116-generic linux-image-extra-4.4.0-21-generic linux-signed-image-4.4.0-116-generic runc ubuntu-fan
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
libdrm-dev libegl1-mesa-dev libgl1-mesa-dev libgles2-mesa libgles2-mesa-dev libglu1-mesa-dev libmirclient-dev libmircommon-dev libmircookie-dev libmircookie2 libmircore-dev libprotobuf-dev libprotobuf9v5
libqt5concurrent5 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5opengl5 libqt5opengl5-dev libqt5printsupport5 libqt5sql5 libqt5sql5-sqlite libqt5svg5 libqt5test5 libqt5widgets5 libqt5xml5 libwayland-bin
libwayland-dev libx11-xcb-dev libxcb-dri2-0-dev libxcb-dri3-dev libxcb-glx0-dev libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present-dev libxcb-randr0 libxcb-randr0-dev libxcb-render-util0 libxcb-render0-dev
libxcb-shape0-dev libxcb-sync-dev libxcb-xfixes0-dev libxcb-xkb1 libxdamage-dev libxext-dev libxfixes-dev libxkbcommon-dev libxkbcommon-x11-0 libxshmfence-dev libxxf86vm-dev mesa-common-dev qt5-qmake
qtbase5-dev-tools qttranslations5-l10n x11proto-damage-dev x11proto-dri2-dev x11proto-fixes-dev x11proto-gl-dev x11proto-xext-dev x11proto-xf86vidmode-dev
Suggested packages:
libqt5libqgtk2 qt5-image-formats-plugins qtwayland5 libxext-doc libmysqlclient-dev libpq-dev libsqlite3-dev unixodbc-dev
The following NEW packages will be installed:
libdrm-dev libegl1-mesa-dev libgl1-mesa-dev libgles2-mesa libgles2-mesa-dev libglu1-mesa-dev libmirclient-dev libmircommon-dev libmircookie-dev libmircookie2 libmircore-dev libprotobuf-dev libprotobuf9v5
libqt5concurrent5 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5opengl5 libqt5opengl5-dev libqt5printsupport5 libqt5sql5 libqt5sql5-sqlite libqt5svg5 libqt5svg5-dev libqt5test5 libqt5widgets5 libqt5xml5
libwayland-bin libwayland-dev libx11-xcb-dev libxcb-dri2-0-dev libxcb-dri3-dev libxcb-glx0-dev libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present-dev libxcb-randr0 libxcb-randr0-dev libxcb-render-util0
libxcb-render0-dev libxcb-shape0-dev libxcb-sync-dev libxcb-xfixes0-dev libxcb-xkb1 libxdamage-dev libxext-dev libxfixes-dev libxkbcommon-dev libxkbcommon-x11-0 libxshmfence-dev libxxf86vm-dev mesa-common-dev
qt5-qmake qtbase5-dev qtbase5-dev-tools qttranslations5-l10n x11proto-damage-dev x11proto-dri2-dev x11proto-fixes-dev x11proto-gl-dev x11proto-xext-dev x11proto-xf86vidmode-dev
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Get:20 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 x11proto-xf86vidmode-dev all 2.3.1-2 [6.116 B]
Get:21 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxxf86vm-dev amd64 1:1.1.4-1 [13,3 kB]
Get:22 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 x11proto-fixes-dev all 1:5.0-2ubuntu2 [14,2 kB]
Get:23 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxfixes-dev amd64 1:5.0.1-2 [10,9 kB]
Get:24 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 x11proto-damage-dev all 1:1.2.1-2 [8.286 B]
Get:25 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxdamage-dev amd64 1:1.1.4-2 [5.028 B]
Get:26 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-glx0-dev amd64 1.11.1-1ubuntu1 [26,9 kB]
Get:27 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-dri2-0-dev amd64 1.11.1-1ubuntu1 [8.384 B]
Get:28 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-dri3-dev amd64 1.11.1-1ubuntu1 [5.752 B]
Get:29 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-render0-dev amd64 1.11.1-1ubuntu1 [15,3 kB]
Get:30 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-randr0-dev amd64 1.11.1-1ubuntu1 [18,2 kB]
Get:31 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-shape0-dev amd64 1.11.1-1ubuntu1 [6.900 B]
Get:32 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-xfixes0-dev amd64 1.11.1-1ubuntu1 [11,2 kB]
Get:33 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-sync-dev amd64 1.11.1-1ubuntu1 [10,1 kB]
Get:34 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxcb-present-dev amd64 1.11.1-1ubuntu1 [6.618 B]
Get:35 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxshmfence-dev amd64 1.2-1 [3.676 B]
Get:36 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libx11-xcb-dev amd64 2:1.6.3-1ubuntu2 [9.730 B]
Get:37 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libwayland-bin amd64 1.12.0-1~ubuntu16.04.3 [18,4 kB]
Get:38 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libwayland-dev amd64 1.12.0-1~ubuntu16.04.3 [92,4 kB]
Get:39 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libmircore-dev amd64 0.26.3+16.04.20170605-0ubuntu1.1 [23,7 kB]
Get:40 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libprotobuf9v5 amd64 2.6.1-1.3 [326 kB]
Get:41 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libprotobuf-dev amd64 2.6.1-1.3 [473 kB]
Get:42 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libxkbcommon-dev amd64 0.5.0-1ubuntu2 [231 kB]
Get:43 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libmircommon-dev amd64 0.26.3+16.04.20170605-0ubuntu1.1 [14,9 kB]
Get:44 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libmircookie2 amd64 0.26.3+16.04.20170605-0ubuntu1.1 [22,5 kB]
Get:45 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libmircookie-dev amd64 0.26.3+16.04.20170605-0ubuntu1.1 [5.152 B]
Get:46 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libmirclient-dev amd64 0.26.3+16.04.20170605-0ubuntu1.1 [42,6 kB]
Get:47 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libegl1-mesa-dev amd64 17.2.8-0ubuntu0~16.04.1 [19,9 kB]
Get:48 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libgles2-mesa amd64 17.2.8-0ubuntu0~16.04.1 [13,4 kB]
Get:49 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libgles2-mesa-dev amd64 17.2.8-0ubuntu0~16.04.1 [40,2 kB]
Get:50 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 mesa-common-dev amd64 17.2.8-0ubuntu0~16.04.1 [525 kB]
Get:51 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libgl1-mesa-dev amd64 17.2.8-0ubuntu0~16.04.1 [4.456 B]
Get:52 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 libglu1-mesa-dev amd64 9.0.0-2.1 [202 kB]
Get:53 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5concurrent5 amd64 5.5.1+dfsg-16ubuntu7.5 [24,3 kB]
Get:54 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5opengl5 amd64 5.5.1+dfsg-16ubuntu7.5 [128 kB]
Get:55 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5printsupport5 amd64 5.5.1+dfsg-16ubuntu7.5 [174 kB]
Get:56 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5sql5 amd64 5.5.1+dfsg-16ubuntu7.5 [104 kB]
Get:57 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5test5 amd64 5.5.1+dfsg-16ubuntu7.5 [84,5 kB]
Get:58 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5xml5 amd64 5.5.1+dfsg-16ubuntu7.5 [91,8 kB]
Get:59 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 qt5-qmake amd64 5.5.1+dfsg-16ubuntu7.5 [1.192 kB]
Get:60 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 qtbase5-dev-tools amd64 5.5.1+dfsg-16ubuntu7.5 [1.056 kB]
Get:61 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 qtbase5-dev amd64 5.5.1+dfsg-16ubuntu7.5 [931 kB]
Get:62 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5opengl5-dev amd64 5.5.1+dfsg-16ubuntu7.5 [31,5 kB]
Get:63 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5sql5-sqlite amd64 5.5.1+dfsg-16ubuntu7.5 [33,0 kB]
Get:64 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 qttranslations5-l10n all 5.5.1-2build1 [1.210 kB]
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Setting up x11proto-xf86vidmode-dev (2.3.1-2) ...
Setting up libxxf86vm-dev:amd64 (1:1.1.4-1) ...
Setting up x11proto-fixes-dev (1:5.0-2ubuntu2) ...
Setting up libxfixes-dev:amd64 (1:5.0.1-2) ...
Setting up x11proto-damage-dev (1:1.2.1-2) ...
Setting up libxdamage-dev:amd64 (1:1.1.4-2) ...
Setting up libxcb-glx0-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-dri2-0-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-dri3-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-render0-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-randr0-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-shape0-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-xfixes0-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-sync-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxcb-present-dev:amd64 (1.11.1-1ubuntu1) ...
Setting up libxshmfence-dev:amd64 (1.2-1) ...
Setting up libx11-xcb-dev:amd64 (2:1.6.3-1ubuntu2) ...
Setting up libwayland-bin (1.12.0-1~ubuntu16.04.3) ...
Setting up libwayland-dev:amd64 (1.12.0-1~ubuntu16.04.3) ...
Setting up libmircore-dev:amd64 (0.26.3+16.04.20170605-0ubuntu1.1) ...
Setting up libprotobuf9v5:amd64 (2.6.1-1.3) ...
Setting up libprotobuf-dev:amd64 (2.6.1-1.3) ...
Setting up libxkbcommon-dev (0.5.0-1ubuntu2) ...
Setting up libmircommon-dev:amd64 (0.26.3+16.04.20170605-0ubuntu1.1) ...
Setting up libmircookie2:amd64 (0.26.3+16.04.20170605-0ubuntu1.1) ...
Setting up libmircookie-dev:amd64 (0.26.3+16.04.20170605-0ubuntu1.1) ...
Setting up libmirclient-dev:amd64 (0.26.3+16.04.20170605-0ubuntu1.1) ...
Setting up libegl1-mesa-dev:amd64 (17.2.8-0ubuntu0~16.04.1) ...
Setting up libgles2-mesa:amd64 (17.2.8-0ubuntu0~16.04.1) ...
Setting up libgles2-mesa-dev:amd64 (17.2.8-0ubuntu0~16.04.1) ...
Setting up mesa-common-dev:amd64 (17.2.8-0ubuntu0~16.04.1) ...
Setting up libgl1-mesa-dev:amd64 (17.2.8-0ubuntu0~16.04.1) ...
Setting up libglu1-mesa-dev:amd64 (9.0.0-2.1) ...
Setting up libqt5concurrent5:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up libqt5opengl5:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up libqt5printsupport5:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up libqt5sql5:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up libqt5test5:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up libqt5xml5:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up qt5-qmake:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up qtbase5-dev-tools (5.5.1+dfsg-16ubuntu7.5) ...
Setting up qtbase5-dev:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
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Setting up libqt5sql5-sqlite:amd64 (5.5.1+dfsg-16ubuntu7.5) ...
Setting up qttranslations5-l10n (5.5.1-2build1) ...
Processing triggers for libc-bin (2.23-0ubuntu10) ...
➜ Tools git:(master) ✗ git clone https://github.com/rrwick/Bandage.git
Cloning into 'Bandage'...
remote: Counting objects: 7813, done.
remote: Total 7813 (delta 0), reused 0 (delta 0), pack-reused 7813
Receiving objects: 100% (7813/7813), 27.43 MiB | 16.33 MiB/s, done.
Resolving deltas: 100% (5973/5973), done.
Checking connectivity... done.
➜ Tools git:(master) ✗ cd Bandage
➜ Bandage git:(master) ls
Bandage.pro BandageTests.pro blast build_scripts command_line COPYING graph images ogdf program README.md tests ui
➜ Bandage git:(master) export QT_SELECT=5
➜ Bandage git:(master) qmake
➜ Bandage git:(master) ✗ make
/home/urbe/anaconda3/bin/uic ui/mainwindow.ui -o ui_mainwindow.h
/home/urbe/anaconda3/bin/uic ui/settingsdialog.ui -o ui_settingsdialog.h
/home/urbe/anaconda3/bin/uic ui/aboutdialog.ui -o ui_aboutdialog.h
/home/urbe/anaconda3/bin/uic ui/enteroneblastquerydialog.ui -o ui_enteroneblastquerydialog.h
/home/urbe/anaconda3/bin/uic ui/blastsearchdialog.ui -o ui_blastsearchdialog.h
/home/urbe/anaconda3/bin/uic ui/myprogressdialog.ui -o ui_myprogressdialog.h
/home/urbe/anaconda3/bin/uic ui/pathspecifydialog.ui -o ui_pathspecifydialog.h
/home/urbe/anaconda3/bin/uic ui/querypathsdialog.ui -o ui_querypathsdialog.h
/home/urbe/anaconda3/bin/uic ui/blasthitfiltersdialog.ui -o ui_blasthitfiltersdialog.h
/home/urbe/anaconda3/bin/uic ui/changenodenamedialog.ui -o ui_changenodenamedialog.h
/home/urbe/anaconda3/bin/uic ui/graphinfodialog.ui -o ui_graphinfodialog.h
/home/urbe/anaconda3/bin/uic ui/changenodedepthdialog.ui -o ui_changenodedepthdialog.h
g++ -c -pipe -O2 -std=gnu++0x -Wall -W -D_REENTRANT -fPIC -DQT_NO_DEBUG -DQT_SVG_LIB -DQT_WIDGETS_LIB -DQT_GUI_LIB -DQT_CORE_LIB -I. -Iui -I/usr/include -I../../anaconda3/include/qt -I../../anaconda3/include/qt/QtSvg -I../../anaconda3/include/qt/QtWidgets -I../../anaconda3/include/qt/QtGui -I../../anaconda3/include/qt/QtCore -I. -I. -I../../anaconda3/mkspecs/linux-g++ -o main.o program/main.cpp
g++ -c -pipe -O2 -std=gnu++0x -Wall -W -D_REENTRANT -fPIC -DQT_NO_DEBUG -DQT_SVG_LIB -DQT_WIDGETS_LIB -DQT_GUI_LIB -DQT_CORE_LIB -I. -Iui -I/usr/include -I../../anaconda3/include/qt -I../../anaconda3/include/qt/QtSvg -I../../anaconda3/include/qt/QtWidgets -I../../anaconda3/include/qt/QtGui -I../../anaconda3/include/qt/QtCore -I. -I. -I../../anaconda3/mkspecs/linux-g++ -o settings.o program/settings.cpp
....

...
g++ -Wl,-O1 -Wl,-rpath,/home/urbe/anaconda3/lib -o Bandage main.o settings.o globals.o graphlayoutworker.o debruijnnode.o debruijnedge.o graphicsitemnode.o graphicsitemedge.o mainwindow.o graphicsviewzoom.o settingsdialog.o mygraphicsview.o mygraphicsscene.o aboutdialog.o enteroneblastquerydialog.o blasthit.o blastqueries.o blastsearchdialog.o infotextwidget.o assemblygraph.o verticalscrollarea.o myprogressdialog.o nodewidthvisualaid.o verticallabel.o load.o image.o commoncommandlinefunctions.o mytablewidget.o buildblastdatabaseworker.o colourbutton.o blastquery.o runblastsearchworker.o blastsearch.o path.o pathspecifydialog.o graphlocation.o tablewidgetitemint.o tablewidgetitemdouble.o tablewidgetitemshown.o memory.o querypathspushbutton.o querypathsdialog.o blastquerypath.o blasthitfiltersdialog.o scinot.o changenodenamedialog.o querypathsequencecopybutton.o querypaths.o info.o reduce.o Graph.o GraphAttributes.o FMMMLayout.o geometry.o ClusterGraphAttributes.o FruchtermanReingold.o NMM.o GmlParser.o simple_graph_alg.o basic.o XmlParser.o String.o Hashing.o PoolMemoryAllocator.o GraphCopy.o CombinatorialEmbedding.o OgmlParser.o ClusterGraph.o Math.o EdgeAttributes.o NodeAttributes.o MAARPacking.o Multilevel.o numexcept.o Set.o Ogml.o DinoXmlParser.o DinoXmlScanner.o DinoTools.o DinoLineBuffer.o System.o QuadTreeNM.o QuadTreeNodeNM.o Constraint.o MultilevelGraph.o graphinfodialog.o tablewidgetitemname.o changenodedepthdialog.o qrc_images.o moc_graphlayoutworker.o moc_mainwindow.o moc_graphicsviewzoom.o moc_settingsdialog.o moc_mygraphicsview.o moc_mygraphicsscene.o moc_aboutdialog.o moc_enteroneblastquerydialog.o moc_blastquery.o moc_blastsearchdialog.o moc_infotextwidget.o moc_assemblygraph.o moc_verticalscrollarea.o moc_myprogressdialog.o moc_nodewidthvisualaid.o moc_verticallabel.o moc_mytablewidget.o moc_buildblastdatabaseworker.o moc_colourbutton.o moc_runblastsearchworker.o moc_pathspecifydialog.o moc_querypathspushbutton.o moc_querypathsdialog.o moc_blasthitfiltersdialog.o moc_changenodenamedialog.o moc_querypathsequencecopybutton.o moc_graphinfodialog.o moc_changenodedepthdialog.o -L/usr/lib -L/home/urbe/anaconda3/lib -lQt5Svg -lQt5Widgets -lQt5Gui -lQt5Core -lGL -lpthread
➜ Bandage git:(master) ✗ ls
aboutdialog.o DinoTools.o Makefile moc_infotextwidget.cpp moc_verticalscrollarea.o scinot.o
assemblygraph.o DinoXmlParser.o Math.o moc_infotextwidget.o MultilevelGraph.o Set.o
Bandage DinoXmlScanner.o memory.o moc_mainwindow.cpp Multilevel.o settingsdialog.o
Bandage.pro EdgeAttributes.o moc_aboutdialog.cpp moc_mainwindow.o mygraphicsscene.o settings.o
BandageTests.pro enteroneblastquerydialog.o moc_aboutdialog.o moc_mygraphicsscene.cpp mygraphicsview.o simple_graph_alg.o
basic.o FMMMLayout.o moc_assemblygraph.cpp moc_mygraphicsscene.o myprogressdialog.o String.o
blast FruchtermanReingold.o moc_assemblygraph.o moc_mygraphicsview.cpp mytablewidget.o System.o
blasthitfiltersdialog.o geometry.o moc_blasthitfiltersdialog.cpp moc_mygraphicsview.o NMM.o tablewidgetitemdouble.o
blasthit.o globals.o moc_blasthitfiltersdialog.o moc_myprogressdialog.cpp NodeAttributes.o tablewidgetitemint.o
blastqueries.o GmlParser.o moc_blastquery.cpp moc_myprogressdialog.o nodewidthvisualaid.o tablewidgetitemname.o
blastquery.o graph moc_blastquery.o moc_mytablewidget.cpp numexcept.o tablewidgetitemshown.o
blastquerypath.o GraphAttributes.o moc_blastsearchdialog.cpp moc_mytablewidget.o ogdf tests
blastsearchdialog.o GraphCopy.o moc_blastsearchdialog.o moc_nodewidthvisualaid.cpp Ogml.o ui
blastsearch.o graphicsitemedge.o moc_buildblastdatabaseworker.cpp moc_nodewidthvisualaid.o OgmlParser.o ui_aboutdialog.h
buildblastdatabaseworker.o graphicsitemnode.o moc_buildblastdatabaseworker.o moc_pathspecifydialog.cpp path.o ui_blasthitfiltersdialog.h
build_scripts graphicsviewzoom.o moc_changenodedepthdialog.cpp moc_pathspecifydialog.o pathspecifydialog.o ui_blastsearchdialog.h
changenodedepthdialog.o graphinfodialog.o moc_changenodedepthdialog.o moc_querypathsdialog.cpp PoolMemoryAllocator.o ui_changenodedepthdialog.h
changenodenamedialog.o graphlayoutworker.o moc_changenodenamedialog.cpp moc_querypathsdialog.o program ui_changenodenamedialog.h
ClusterGraphAttributes.o graphlocation.o moc_changenodenamedialog.o moc_querypathsequencecopybutton.cpp qrc_images.cpp ui_enteroneblastquerydialog.h
ClusterGraph.o Graph.o moc_colourbutton.cpp moc_querypathsequencecopybutton.o qrc_images.o ui_graphinfodialog.h
colourbutton.o Hashing.o moc_colourbutton.o moc_querypathspushbutton.cpp QuadTreeNM.o ui_mainwindow.h
CombinatorialEmbedding.o image.o moc_enteroneblastquerydialog.cpp moc_querypathspushbutton.o QuadTreeNodeNM.o ui_myprogressdialog.h
command_line images moc_enteroneblastquerydialog.o moc_runblastsearchworker.cpp querypathsdialog.o ui_pathspecifydialog.h
commoncommandlinefunctions.o info.o moc_graphicsviewzoom.cpp moc_runblastsearchworker.o querypathsequencecopybutton.o ui_querypathsdialog.h
Constraint.o infotextwidget.o moc_graphicsviewzoom.o moc_settingsdialog.cpp querypaths.o ui_settingsdialog.h
COPYING load.o moc_graphinfodialog.cpp moc_settingsdialog.o querypathspushbutton.o verticallabel.o
debruijnedge.o MAARPacking.o moc_graphinfodialog.o moc_verticallabel.cpp README.md verticalscrollarea.o
debruijnnode.o main.o moc_graphlayoutworker.cpp moc_verticallabel.o reduce.o XmlParser.o
DinoLineBuffer.o mainwindow.o moc_graphlayoutworker.o moc_verticalscrollarea.cpp runblastsearchworker.o
➜ Bandage git:(master) ✗ ./Bandage

Scientists map 17,294 proteins produced in human body

Jit — Thu, 29 May 2014 01:57:55 -0500

Indian scientists missed the genomic profiling bus, but they've more than made up for it by creating the first human proteome map which is an extension of the genomic study. Till now, here is no direct equivalent for the human proteome. But recently two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome.

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB