BOL: Related items

Bpipe - a tool for running and managing bioinformatics pipelines

Radha Agarkar — Sat, 21 May 2016 22:42:16 -0500

Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.

January 20th, 2016 - New! Bpipe 0.9.9 released!
Download latest, all
Documentation
Mailing List (Google Group)

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

Genome Workbench 2.10.7

Gudiya Pal — Fri, 01 Jul 2016 12:09:59 -0500

Genome Workbench 2.10.7 is here! New features include added support for local custom BLAST databases and improvements to Tree View.

For the full list of features, improvements and fixes, see the release notes:https://ncbi.nlm.nih.gov/tools/gbench/releasenotes

New Features

BLAST Tool: added support for local custom BLAST databases
Graphical Sequence View: added log scaling option for graph tracks
Generic Table View: new tutorial added

Bug Fixes and Improvements

Project Tree View: Genomic Collections/Assemblies now show accessions, not just names
Tree View: layout updated to better accommodate nodes of different sizes
Table Import Dialog (MacOS): fixed issue with table visibility
Fixed bug where different molecules IDs in GenBank could resolve to the same sequence
Graphical Sequence View: fixed issue where sequence track was not shown for some sequences
Graphical Sequence View: fixed protein coloration methods
Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images
Create Gene Model tool: fixed scenario when gene model tool failed with local sequences
Search View: ORF Finder – fixed incorrect protein lengths
Fixed bug with not opening project file (.gbp) on a click
Fixed issues in GVF import
Fixed BLAST Search tool against NCBI databases not working
Fixed tblastn (protein BLAST) not working in standalone mode
Fixed GTF export failure

Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory

Wed, 18 Aug 2021 00:19:48 -0500

Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

Chemical Elements of Bioinformatics

Rahul Agarwal — Tue, 03 Sep 2013 16:35:39 -0500

You must be familiar with periodic table and colour pattern, but this time you are going to amaze by new elements table by Eagle genomics. Just check it out and have fun :)

http://elements.eaglegenomics.com/

Assistant Professor (Bio-Informatics) at Health and Family Welfare Department (Medical Education) in Raipur

Wed, 07 May 2014 00:08:38 -0500

Advertisement No.05/2014/ Exam/Dated 17/04/2014

No of vacancies: 01

Pay scale:Rs. 15600 – 39100 + 6600/-

Essential Academic Qualifications / Experience : Good academic record as defined by the concerned university with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed) at the Master's Degree level in a relevant subject from an Indian University, or an equivalent degree from an accredited foreign university.

Besides fulfilling the above qualifications, the candidate must have cleared the National Eligibility Test (NET) conducted by the UGC, CSIR or similar test accredited by the UGC like SLET/ SET.

Notwithstanding anything contained in sub-clauses (a) and (b) to this Clause, candidates, who are, or have been awarded a Ph.D. Degree in accordance with the University Grants Commission (Minimum Standards and Procedure for Award of Ph.D. Degree) Regulations, 2009, shall be exempted from the requirement of the minimum eligibility condition of NET/SLET/SET for recruitment and appointment of Assistant Professor or equivalent positions in Universities/Colleges/Institutions.

NET/SLET/SET shall also not be required for such Masters Programmes in disciplines for which NET/SLET/SET is not conducted.

Apply online: http://www.psc.cg.gov.in/htm/OA_ME2014.html

Last Date for Online Registration: 22/05/2014

For more details: http://www.psc.cg.gov.in/pdf/Advertisement/ADV_ME2014.pdf

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.

GPS DNA tracking - University of Sheffield

Sat, 10 May 2014 04:33:28 -0500

University of Sheffield geneticist and bioinformatics expert Dr Eran Elhaik demonstrates the power of his new DNA research, which allows people to discover their genetic homeland from 1000 years ago. Find out more about our biological research here http://www.sheffield.ac.uk/aps

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

Managing and Analyzing Next-Generation Sequence Data

Rahul Agarwal — Sat, 10 May 2014 06:28:06 -0500

Centralized Bioinformatics Core Facilities provide shared resources for the computational and IT requirements of the investigators in their department or institution. As such, they must be able to effectively react to new types of experimental technology. Recently faced with an unprecedented flood of data generated by the next generation of DNA sequencers, these groups found it necessary to respond quickly and efficiently to the informatics and infrastructure demands. Centralized Facilities newly facing this challenge need to anticipate time and design considerations of necessary components, including infrastructure upgrades, staffing, and tools for data analyses and management ...

More at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369

Address of the bookmark: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369

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+