BOL: Related items

Science for Life Laboratory (SciLifeLab)-Sweden

Sat, 10 May 2014 06:22:30 -0500

Science for Life Laboratory (SciLifeLab) is a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a national resource and a collaboration between four universities: Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University and Uppsala University.

Webpage : https://www.scilifelab.se/about-us/
Opportunity: https://www.scilifelab.se/about-us/career/

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.

RA at IISR Kozhikode

Thu, 15 May 2014 10:08:09 -0500

INDIAN INSTITUTE OF SPICES RESEARCH
(Indian Council of Agricultural Research)
Marikunnu P.O., Kozhikode – 673 012, Kerala

Walk- in- Test cum Interview (based on test) for the selection of Research Associate

under the scheme “Distributed Information Sub Centre –DISC” & Research Assistant under scheme “Phytophthora, Fusarium and Ralstonia diseases of Horticultural and Field Crops” will be held at this Institute as per details indicated below.

WALK -IN- TEST CUM INTERVIEW

Name of the post : Research Associate

Date of Interview : 21-05-2014 at 10.00 AM

No. of posts : One

Qualifications : a)Essential

Ph.D Degree in Bioinformatics OR : Masters degree in Bioinformatics with a minimum of
60% marks or equivalent OGPA with at least two years research experience as evidenced from fellowship/ associateship/training/published papers etc.

b)Desirable: Experience in NGS data analysis.

Emoluments : Rs. 23,000/- per month + HRA (Masters Degree Holders)

Rs. 24,000/- per month + HRA (Ph.D Degree Holders)

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till 31-03-2017.

Title of Assigment : Research Assistant (on contract basis)

No. of vacancy : One

Qualification : Essential : Post Graduation in Bioinformatics and Minimum one year experience in NGS data analysis

Desirable : Experience in Perl/Python/R

Remuneration : Rs. 20,000/- per month (consolidated)

Scope of work :

1. Analysis of different file formats and their conversions.

2. Assessing the quality of data and filtering of raw reads.
3. Assembling the raw reads-de novo as well as reference mapping.
4. Compression of aligned reads using Jam tools
5. RNA-seq. Analysis
6. Differential expression testing involving Normalization, Statistical testing, heat map generation & hierarchical clustering
7. Annotating the assembled genome and geneet testing and their validation
8. Metabolic pathway analysis
9. Comparative genomics
10. Setting up of genome browsers.

Period of Assigment : Initially for six months.

Date & Venue of Interview : 21-05-2014 at IISR, Kozhikode at 10.00 AM

More at http://www.spices.res.in/pdf/disc-advtmnt.pdf

A fast package to parse BLAST

Jitendra Narayan — Tue, 10 Sep 2013 16:58:56 -0500

In current era, we are handling huge amount of genomics data, and analysing it to make some biological sense out of it. Large-scale sequence studies requiring BLAST-based analysis produce huge amounts of data to be parsed. There are several BLAST parsers are available, but they are often missing some important features, such as keeping all information from the raw BLAST output, allowing direct access to single results, and performing logical operations over them.

Massimiliano Orsini and Simone Carcangiu develope a new and fast fast package "BlaSTorage" to parse and store BLAST results. BlaSTorage shows comparable speed of more basic parser written in compiled languages as C++ and can be easily integrated into web applications or software pipelines.

Find more @ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571973/

http://biowiki.crs4.it/biowiki/MassimilianoOrsini

Professor/Associate Professor/ Assistant Professor at Chettinad Academy of Research and Education

Sat, 24 May 2014 00:00:15 -0500

OPEN FACULTY POSITION

Chettinad Academy of Research and Education (CARE) invites applications from eligible and translational research-oriented candidates to the posts of Professor/Associate Professor/ Assistant Professor Computational Biology, Bioinformatics, and Pharmaceutical Chemistry.

Emoluments: As per UGC norms (Adequate Compensation for Postdoctoral/Teaching experience)

Candidates fulfilling the eligibility criteria as per the UGC norms can send their full CV with copies of certificates and reference letters to the following address by post or by e-mail on or before 31st May 2014

The Registrar,
Chettinad Academy of Research and Education,
Chettinad Health City
Kelambakkam, Chennai 603 103
Tamil Nadu
T +91 (0)44 4741 1000
F +91 (0)44 4741 1011
Email: jobs @chettinadhealthcity.com

Advertisement: http://182.73.176.163/chc/ads2014.pdf

Basic command-line to run BLAST

Shruti Paniwala — Wed, 14 Mar 2018 05:10:34 -0500

The goal of this tutorial is to run you through a demonstration of the command line, which you may not have seen or used much before.

All of the commands below can copy/pasted.

Install software

Copy and paste the following commands

sudo apt-get update && sudo apt-get -y install python ncbi-blast+

This updates the software list and installs the Python programming language and NCBI BLAST+.

Get Data

Grab some data to play with. Grab some cow and human RefSeq proteins:

wget ftp://ftp.ncbi.nih.gov/refseq/B_taurus/mRNA_Prot/cow.1.protein.faa.gz
wget ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/mRNA_Prot/human.1.protein.faa.gz

This is only the first part of the human and cow protein files - there are 24 files total for human.

The database files are both gzipped, so lets unzip them

gunzip *gz
ls

Take a look at the head of each file:

head cow.1.protein.faa
head human.1.protein.faa

These are protein sequences in FASTA format. FASTA format is something many of you have probably seen in one form or another – it’s pretty ubiquitous. It’s just a text file, containing records; each record starts with a line beginning with a ‘>’, and then contains one or more lines of sequence text.

Note that the files are in fasta format, even though they end if ”.faa” instead of the usual ”.fasta”. This NCBI’s way of denoting that this is a fasta file with amino acids instead of nucleotides.

How many sequences are in each one?

grep -c '^>' cow.1.protein.faa
grep -c '^>' human.1.protein.faa

This grep command uses the c flag, which reports a count of lines with match to the pattern. In this case, the pattern is a regular expression, meaning match only lines that begin with a >.

This is a bit too big, lets take a smaller set for practice. Lets take the first two sequences of the cow proteins, which we can see are on the first 6 lines

head -6 cow.1.protein.faa > cow.small.faa

BLAST

Now we can blast these two cow sequences against the set of human sequences. First, we need to tell blast about our database. BLAST needs to do some pre-work on the database file prior to searching. This helps to make the software work a lot faster. Because you installed your own version of the sotware, you need to tell the shell where the software is located. Use the full path and the makeblastdb command:

makeblastdb -in human.1.protein.faa -dbtype prot
ls

Note that this makes a lot of extra files, with the same name as the database plus new extensions (.pin, .psq, etc). To make blast work, these files, called index files, must be in the same directory as the fasta file.

blastp [-h] [-help] [-import_search_strategy filename]
[-export_search_strategy filename] [-task task_name] [-db database_name]
[-dbsize num_letters] [-gilist filename] [-seqidlist filename]
[-negative_gilist filename] [-negative_seqidlist filename]
[-entrez_query entrez_query] [-db_soft_mask filtering_algorithm]
[-db_hard_mask filtering_algorithm] [-subject subject_input_file]
[-subject_loc range] [-query input_file] [-out output_file]
[-evalue evalue] [-word_size int_value] [-gapopen open_penalty]
[-gapextend extend_penalty] [-qcov_hsp_perc float_value]
[-max_hsps int_value] [-xdrop_ungap float_value] [-xdrop_gap float_value]
[-xdrop_gap_final float_value] [-searchsp int_value]
[-sum_stats bool_value] [-seg SEG_options] [-soft_masking soft_masking]
[-matrix matrix_name] [-threshold float_value] [-culling_limit int_value]
[-best_hit_overhang float_value] [-best_hit_score_edge float_value]
[-window_size int_value] [-lcase_masking] [-query_loc range]
[-parse_deflines] [-outfmt format] [-show_gis]
[-num_descriptions int_value] [-num_alignments int_value]
[-line_length line_length] [-html] [-max_target_seqs num_sequences]
[-num_threads int_value] [-ungapped] [-remote] [-comp_based_stats compo]
[-use_sw_tback] [-version]

Now we can run the blast job. We will use blastp, which is appropriate for protein to protein comparisons.

blastp -query cow.small.faa -db human.1.protein.faa

This gives us a lot of information on the terminal screen. But this is difficult to save and use later - Blast also gives the option of saving the text to a file.

    blastp -query cow.small.faa -db human.1.protein.faa -out cow_vs_human_blast_results.txt
ls

Take a look at the results using less. Note that there can be more than one match between the query and the same subject. These are referred to as high-scoring segment pairs (HSPs).

less cow_vs_human_blast_results.txt

So how do you know about all the options, such as the flag to create an output file? Lets also take a look at the help pages. Unfortunately there are no man pages (those are usually reserved for shell commands, but some software authors will provide them as well), but there is a text help output

blastp -help

To scroll through slowly

blastp -help | less

To quit the less screen, press the q key.

Parameters of interest include the -evalue (Default is 10?!?) and the -outfmt

Lets filter for more statistically significant matches with a different output format:

blastp \
-query cow.small.faa \
-db human.1.protein.faa \
-out cow_vs_human_blast_results.tab \
-evalue 1e-5 \
-outfmt 7

I broke the long single command into many lines with by “escaping” the newline. That forward slash tells the command line “Wait, I’m not done yet!”. So it waits for the next line of the command before executing.

Check out the results with less.

Lets try a medium sized data set next

head -199 cow.1.protein.faa > cow.medium.faa

What size is this db?

grep -c '^>' cow.medium.faa

Lets run the blast again, but this time lets return only the best hit for each query.

blastp \
-query cow.medium.faa \
-db human.1.protein.faa \
-out cow_vs_human_blast_results.tab \
-evalue 1e-5 \
-outfmt 6 \
-max_target_seqs 1

Summary

Review:

command line programs such as blast use flags to get information about how and what to do
blast options can be found by typing blastp -help
break a command up over many lines by using `` to “escape” the new line

Blastn

blastn [-h] [-help] [-import_search_strategy filename]
[-export_search_strategy filename] [-task task_name] [-db database_name]
[-dbsize num_letters] [-gilist filename] [-seqidlist filename]
[-negative_gilist filename] [-negative_seqidlist filename]
[-entrez_query entrez_query] [-db_soft_mask filtering_algorithm]
[-db_hard_mask filtering_algorithm] [-subject subject_input_file]
[-subject_loc range] [-query input_file] [-out output_file]
[-evalue evalue] [-word_size int_value] [-gapopen open_penalty]
[-gapextend extend_penalty] [-perc_identity float_value]
[-qcov_hsp_perc float_value] [-max_hsps int_value]
[-xdrop_ungap float_value] [-xdrop_gap float_value]
[-xdrop_gap_final float_value] [-searchsp int_value]
[-sum_stats bool_value] [-penalty penalty] [-reward reward] [-no_greedy]
[-min_raw_gapped_score int_value] [-template_type type]
[-template_length int_value] [-dust DUST_options]
[-filtering_db filtering_database]
[-window_masker_taxid window_masker_taxid]
[-window_masker_db window_masker_db] [-soft_masking soft_masking]
[-ungapped] [-culling_limit int_value] [-best_hit_overhang float_value]
[-best_hit_score_edge float_value] [-window_size int_value]
[-off_diagonal_range int_value] [-use_index boolean] [-index_name string]
[-lcase_masking] [-query_loc range] [-strand strand] [-parse_deflines]
[-outfmt format] [-show_gis] [-num_descriptions int_value]
[-num_alignments int_value] [-line_length line_length] [-html]
[-max_target_seqs num_sequences] [-num_threads int_value] [-remote]
[-version]

DESCRIPTION
Nucleotide-Nucleotide BLAST 2.7.0+

The Minerva Research Group for Bioinformatics

Tue, 27 May 2014 15:48:14 -0500

The focus of the bioinformatics group is to use computational approaches to gain an insight into genome evolution in primates.

http://www.eva.mpg.de/genetics/bioinformatics/overview.html?Fsize=0%2C%20%40%2F%27

Kelso Group
Department of Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig
Germany
Phone: +49 341 3550 500

Job:
http://www.eva.mpg.de/genetics/bioinformatics/jobs.html?Fsize=0%2C%2B%40

New Layout for BLAST ftp Database Site

Jit — Tue, 21 Jan 2020 11:57:11 -0600

As announced previously, the new default database version for BLAST+ is dbV5. To complete this transition, the ftp database site will be updated to support this change. We expect this change to happen around February 4^th, please adjust your scripts or procedures accordingly.

Here is a list of what is changing:

All databases at the root level will be dbV5.
The dbV5 file naming, “_v5” will be removed. Databases with no “_vX” descriptor will be dbV5.
dbV4 tarballs will be renamed with "_v4", files included in tarball will not be renamed.
dbV4 databases will be moved to a v4 subdirectory.
As of 1/13/20 the Cloud directory will be frozen with no more new entries.
The will be no more updates to dbV4 databases.
The FASTA directory will contain nr, nt, swissprot, and pdbaa files.

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

Monitor running jobs on Linux server

Jitendra Narayan — Fri, 06 Jun 2014 16:18:43 -0500

You as a bioinformatican run lots of program on your servers. Sometime the shared server is also used by your colleague. If server is busy you sometime need to check the running programs and want to monitor the running programs as well. The "top" command will come in handy when you need to find out if things are still running, how long they’ve been running, or how much memory is being used.

‘top’ is very simple to run: type

%% top

You’ll get a screen that looks like this, and is updated regularly:

Simple, right? Heh.

First! Note that you can use ‘q’ or ‘CTRL-C’ to exit from ‘top’.

Now let’s read and understand at each line independently.

The first line:

top - 23:00:48 up 39 days, 2 user, load average: 0.00, 0.00, 0.00

The first line tells you the current time, how long the machine has been up, how many users are logged in, and the short/medium/long-term compute load on the machine. If you run something for a long time, you’ll see these numbers go up. Right now, the machine is basically just sitting there, so these are all close to 0.

The second line:

Tasks: 239 total,   1 running, 238 sleeping,   0 stopped,   0 zombie

This line tells you how many processes are running. If you are using laptops machines it’s not so interesting because you really are the only one using this machine.

Cpu(s): 0.0%us, 0.0%sy, 0.0%ni,100.0%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st

This line contains the CPU load. The first two numbers are how busy the system is doing computation (“us” stands for “user”) and how busy the system is doing system-y things like accessing disks or network (“sy” stands for “system”). We’ll talk more about this later.

Mem:   49457320k total,    3492174k used, 14535596k free,    1435148k buffers

This should be easy to understand – how much memory you’re using!

Swap:   539356k total,   28332k used,   836562k free,    29862014k cached

Swap is just on-disk memory that can be used to “swap” out programs from main memory. Again, we’ll talk about this later.:

PID USER      PR NI VIRT RES SHR S %CPU %MEM    TIME+ COMMAND
1 root      39 19 0 0 0 S 0.0 0.0   246:57.22 kipmi0
2 root      RT   0     0    0    0 S 0.0 0.0   0:00.00 migration/0

And... finally! What’s actually running! The two most important numbers are the %CPU and %MEM towards the right, as well as the COMMAND. This tells you how compute- and memory-intensive your program is. Right now, nothing’s running so the numbers aren’t very interesting, but just wait until we run something...

Genomics and Personalized Medicine Breakthroughs

Sun, 01 Jun 2014 23:40:14 -0500

http://bit.ly/e8QGzY Human genome mapping is now enabling a breakthrough in medical innovation -- personalized medicine. What does this mean for patients? We can now identify predispositions to disease, predict how we metabolize drugs, and figure out what kinds of treatments we may respond to, and even determine when a drug may give us an adverse reaction. All medical specialties benefit from human genome intelligence -- oncology saw the first impacts -- but advances are now being seen in cardiology, obstetrics and gynecology, pediatric diseases, gastroenterology, rheumatology, immunology and other areas. This video covers the areas that genetic medicine is impacting and where the future of genomic medicine is heading.