BOL: Related items

Genomics for Bioinformatician

Jitendra Narayan — Sat, 20 Jul 2013 07:03:00 -0500

Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology or genetics and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.

Genomics was established by Fred Sanger when he first sequenced the complete genomes of a virus and a mitochondrion. His group established techniques of sequencing, genome mapping, data storage, and bioinformatic analyses in the 1970-1980s. A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics. Study of the full set of proteins in a cell type or tissue, and the changes during various conditions, is called proteomics. A related concept is materiomics, which is defined as the study of the material properties of biological materials (e.g. hierarchical protein structures and materials, mineralized biological tissues, etc.) and their effect on the macroscopic function and failure in their biological context, linking processes, structure and properties at multiple scales through a materials science approach. The actual term 'genomics' is thought to have been coined by Dr. Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, ME) over beer at a meeting held in Maryland on the mapping of the human genome in 1986.

The outcome of almost two years of intense discussions with literally hundreds of scientists and members of the public, has three major areas of focus: Genomics to Biology, Genomics to Health, and Genomics to Society.

Genomics to Biology:
The human genome sequence provides foundational information that now will allow development of a comprehensive catalog of all of the genome's components, determination of the function of all human genes, and deciphering of how genes and proteins work together in pathways and networks.

Genomics to Health:
Completion of the human genome sequence offers a unique opportunity to understand the role of genetic factors in health and disease, and to apply that understanding rapidly to prevention, diagnosis, and treatment. This opportunity will be realized through such genomics-based approaches as identification of genes and pathways and determining how they interact with environmental factors in health and disease, more precise prediction of disease susceptibility and drug response, early detection of illness, and development of entirely new therapeutic approaches.

Genomics to Society:
Just as the HGP has spawned new areas of research in basic biology and in health, it has created new opportunities in exploring the ethical, legal, and social implications (ELSI) of such work. These include defining policy options regarding the use of genomic information in both medical and non-medical settings and analysis of the impact of genomics on such concepts as race, ethnicity, kinship, individual and group identity, health, disease, and "normality" for traits and behaviors.

This vision for the future of genomics is not just about the NHGRI. It encompasses the whole field of genomics, including the work of all the other Institutes and Centers at the NIH and of a number of other federal agencies. All of the NIH Institutes are already taking full advantage of the sequence and will apply its data to the better understanding of both rare and common diseases, almost all of which have a genetic component. A recent example of the way that the HGP and the knowledge and new technologies it has spawned are already facilitating science is the extremely rapid sequencing by groups in Canada and at the Centers for Disease Control and Prevention (CDC) in Atlanta of the genome of the virus that causes Severe Acute Respiratory Syndrome (SARS). The sequencing of the SARS virus genome provides insight into this new and deadly disease at a speed never before possible in science. In turn, this should lead to the rapid development of diagnostic tests and, in time, vaccines and effective treatments.

Links for the addition material available on Net

Genomes and genomics:

Bioinformatics and Genomics:

Structural genomics tutorial:

Comparative Genomics Tutorial:

GENOME TUTORIAL:

Tools and resources for identifying protein families, domains and motifs

Bioinformatics Tools
Tips, Tutorials, and Terminology for Using Selected Resources in Genome Database Guide:

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes:

Free Online Tutorials Teach Anyone How to Use Genome Databases:

Circos to create concise, explanatory, unique and print-ready visualizations of your data:

Genomics and Comparative Genomics Learning Module:

Computational Challenges in Comparative Genomics

A Tutorial:

A Comparative Genomics Resource for Grains:

PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants:

VISTA :

Software for Genomics

Artemis Artemis is a free genome viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.
Chromas It will display and prints chromatogram files from ABI automated DNA sequencers, and Staden SCF files which the analysis programs for ALF, Li-Cor and Visible Genetics OpenGene sequencers can create.
Glimmer A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DN
Glimmer HMM A fast and accurate gene finder based on a GHMM architecture, developed specifically for eukaryotes. It incorporates splice site models adapted from the GeneSplicer program and uses interpolated Markov models for evaluating the coding regions.
Glimmer M A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programming algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The d
MUMmer MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form.
pDRAW pDRAW32 is being developed as a free time hobby project. It is far from finished, but as it has reached a point where it could be helpful for many labs, it is now available to the scientific community.
Sequin Sequin is a stand-alone software tool developed by the NCBI for submitting and updating entries to the GenBank, EMBL, or DDBJ sequence databases. It is capable of handling simple submissions that contain a single short mRNA sequence, and complex submissio
Staden The Staden Package consists of a series of tools for DNA sequence preparation (pregap4), assembly (gap4), editing (gap4) and DNA/protein sequence analysis (spin).

For more software @ http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools

World promising health companies !

Rahul Nayak — Tue, 31 Dec 2019 19:10:13 -0600

The health care industry is expected to sustain stable growth over the next decade for a variety of reasons. Advances in medicine have prolonged the average lifespans of most people, requiring more health care treatments over longer terms. In years past, once people turned 65 and enrolled in Medicare, they were expected to live another 10 to 20 years.

Biohub is a joint collaborative effort by Berkeley, UCSF and Stanford for a medical science research center funded by a $600 million commitment from Facebook CEO and founder Mark Zuckerberg and his wife Priscilla Chan. It is trademarked as well as CZ Biohub. It is currently co-led by Stephen Quake and Joseph DeRisi.

More at https://www.czbiohub.org/

Calico LLC is an American research and development biotech company founded on September 18, 2013 by Bill Maris and backed by Google with the goal of combating aging and associated diseases. In Google's 2013 Founders' Letter, Larry Page described Calico as a company focused on "health, well-being, and longevity".

More at https://www.calicolabs.com/

UnitedHealth Group, Inc. (UNH) is the largest health care services company in the world, serving over 50 million individuals in the United States as of late 2018 and 5 million in Brazil. The company provides a wide range of health care products and services, such as health maintenance organizations (HMOs), point of service plans (POS), preferred provider organizations (PPOs), and managed fee-for-service programs.

More at https://www.unitedhealthgroup.com/

Illumina Smartphone Chip !!!

Robert M Willioms — Tue, 30 Dec 2014 23:19:54 -0600

Illumina, the company that claims it brought human genome sequencing down to $1000 prices, has now turned its attention to a consumer product - a chip that you can plug into your smartphone and have it read your genetic information.

The biggest challenge ahead of Illumina is simplifying the process of genetic sequencing. Currently, Illumina’s DNA sequencers are gigantic machines that use techinques like colorimetry to work, but while the core technology is computational, it takes some 30 steps to extract genetic data and run it through. This process will likely have to be hugely simplified on mobile devices, given the fact that some studies require extracting 10 mililiters of blood. Illumina researchers are also working on finding the optimal technology for this on-chip DNA sequencing - be it electrical, optical, or other.

Illumina is one of the most prominent names in genetics, often said to be the Intel of genetic sequencing, as just like Intel it provides the algorithms, the processing brain that runs a DNA reading task.

In other recent smartphone-related biotech news, drug company Pfizer launched its REMOTE project, a new type of clinical trial that does not require going to a hospital for checks - targeted at patients with overactive bladder problems, the FDA-approved REMOTE project allowed to gather data from patients from over 10 states remotely, via mobile devices.

This is indeed the Illumina answer to Apple's Health app, HealthBook, Google HealthFit.

The Global Alliance for Genomics and Health (GA4GH)

Jit — Sat, 08 Feb 2020 07:37:31 -0600

The Global Alliance for Genomics and Health (GA4GH) is a policy-framing and technical standards-setting organization, seeking to enable responsible genomic data sharing within a human rights framework.

GA4GH core funders and sponsors enable our work and allow us to convene the international genomic data sharing community.

https://www.ga4gh.org/

Address of the bookmark: https://www.ga4gh.org/

Petrol from Ecoli

Rahul Agarwal — Mon, 30 Sep 2013 10:31:17 -0500

"In recently published paper (entitled "Microbial Production of Short-chain Alkanes") in Nature journal on September 29, a group of genius Korean researchers led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at the Korea Advanced Institute of Science and Technology (KAIST) reported, for the first time, the development of a novel strategy for microbial gasoline production through metabolic engineering of E. coli.

The research team engineered the fatty acid metabolism to provide the fatty acid derivatives that are shorter than normal intracellular fatty acid metabolites, and introduced a novel synthetic pathway for the biosynthesis of short-chain alkanes. This allowed the development of platform E. coli strain capable of producing gasoline for the first time. Furthermore, this platform strain, if desired, can be modified to produce other products such as short-chain fatty esters and short-chain fatty alcohols."

Find more at

http://www.youtube.com/watch?v=8KDXYMIgAi0

http://www.gbcghana.com/index.php?id=1.1550084

http://www.sciencedaily.com/releases/2013/09/130929142737.htm

Paper:

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12536.html

Image source : Wikipedia

16sRNA Database Download

LEGE — Wed, 24 Apr 2024 04:33:15 -0500

Downloading 16S rRNA databases can be crucial for various bioinformatics analyses, especially in microbiome research. However, it's important to note that databases can vary based on your specific needs, such as the taxonomic coverage you require or the type of analysis you're performing. Here's a general guideline on how you can obtain 16S rRNA databases:

NCBI (National Center for Biotechnology Information):
- NCBI provides various databases related to genetic information, including 16S rRNA sequences.
- You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/).
- Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.
- You can download sequences individually or in batches using the provided tools.
GreenGenes:
- GreenGenes is a widely used 16S rRNA gene sequence database.
- You can access it at http://greengenes.secondgenome.com/.
- GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.
SILVA:
- SILVA (https://www.arb-silva.de/) is another comprehensive database for ribosomal RNA (rRNA) sequences.
- It covers not only 16S rRNA but also other ribosomal RNA sequences.
- SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.
Ribosomal Database Project (RDP):
- RDP (http://rdp.cme.msu.edu/) is a curated database that offers 16S rRNA sequences.
- It provides tools for sequence analysis and classification.
- You can download sequences and taxonomy information from their website.
QIIME (Quantitative Insights Into Microbial Ecology):
- QIIME (https://qiime2.org/) is a widely used bioinformatics platform for microbiome analysis.
- It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.
- QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.

Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.

NCBI 16s RNA database location ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz

The Home Microbiome Project

Tue, 02 Sep 2014 15:21:49 -0500

The Home Microbiome Project is an initiative aimed at uncovering the dynamic co-associations between people's bacteria and the bacteria found in their homes.The hope is that the data and project will show that routine monitoring of the microbial diversity of your body and of the environment in which you live is possible. Computer animation courtesy the Biology & Built Environment (BioBE) Center, University of Oregon and Cameron Slayden at Cosmocyte. http://vimeo.com/90059732 BioBE on Vimeo: http://vimeo.com/user22991553

REGEN: Ancestral Genome Reconstruction for Bacteria

Rahul Nayak — Tue, 06 Mar 2018 05:02:36 -0600

REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order.

Address of the bookmark: http://www.mdpi.com/2073-4425/3/3/423

SiLiX: implements an ultra-efficient algorithm for the clustering of homologous sequences

Jit — Wed, 12 Dec 2018 09:22:41 -0600

The software package SiLiX implements an ultra-efficient algorithm for the clustering of homologous sequences, based on single transitive links (single linkage) with alignment coverage constraints.

SiLiX adopts a graph-theoretical framework to interpret similarity pairs as edges of a network. A very efficient algorithm, based on the Disjoint Sets Data Structure, allows the computation of sequence families with low time and space requirements.

A parallel version of SiLiX, based on MPI, is also available in this package and has been proved to be scalable, so that its allows the study of very large datasets.

SiLiX is already included in the analysis pipeline for HOGENOM.

Address of the bookmark: http://lbbe.univ-lyon1.fr/SiLiX?lang=fr

Trust But Verify: Sequencing Your Cell Lines Might Reveal an Uninvited Guest

LEGE — Wed, 04 Jun 2025 00:07:57 -0500

High-throughput sequencing has become indispensable in cell biology, enabling detailed insights into chromatin structure, gene expression, and regulatory dynamics. Yet, when faced with unexpectedly low mapping rates to the human genome, researchers often rush to troubleshoot technical parameters—sequencer quality, adapter trimming, or aligner settings.

Before you go down that path, consider this critical biological question:
Are you sequencing human cells—or bacterial contamination?

The Silent Saboteur: Mycoplasma in Cell Cultures

Mycoplasma contamination remains one of the most widespread and underdiagnosed issues in tissue culture work. Studies suggest that 15–35% of cell lines in use may be contaminated, often without visible signs. Unlike other microbial infections, Mycoplasma does not produce cloudiness, odor, or a change in pH. Many researchers won’t detect it unless they specifically test for it.

The consequences, however, are profound. Mycoplasma can significantly alter:

Host gene expression patterns
Cell proliferation rates
Epigenetic profiles and chromatin accessibility
Cytokine signaling and immune responses

In short, it can skew your results, compromise your biological conclusions, and invalidate weeks or months of research.

A Simple Diagnostic Step: Map Against Mycoplasma Genomes

If you encounter poor alignment rates to the human genome, consider mapping your reads to a Mycoplasma reference genome—or better yet, use a combined human + Mycoplasma reference. There have been cases where over half of all reads, initially assumed to be from human cells, were in fact bacterial in origin. This check is fast, easy, and could save your project.

How Contamination Happens—and Persists

Mycoplasma is small (0.1–0.3 μm), lacks a cell wall, and can pass through standard filters undetected. Common sources include:

Contaminated reagents (e.g., FBS)
Infected cell lines obtained from other labs
Poor aseptic technique or shared equipment

Once present, it spreads quickly between cultures and can persist for months, silently affecting results.

Why Treatment Is Difficult

While antibiotics such as Plasmocin or BM-Cyclin are sometimes used, they often offer only partial resolution and may themselves alter cell behavior. In many cases, the best course of action is to discard the contaminated culture and start with a fresh, verified stock.

Practical Recommendations for Researchers

Routinely test for Mycoplasma using PCR, qPCR, or fluorescence-based assays
Incorporate contamination screens into your sequencing QC pipeline
Use combined reference genomes when mapping ambiguous reads
Practice strict aseptic technique and monitor all incoming cell lines
Don’t ignore unexplained data anomalies—they might point to contamination

Closing Thought: Contamination Is a Biological Variable

It’s easy to view poor mapping as a technical issue, but sometimes the problem lies deeper—in the biology itself. Mycoplasma contamination doesn’t just interfere with sequencing; it interferes with science. As a research community, we must treat contamination not as an afterthought, but as a key variable to control.

So next time your reads won’t align, don’t just tune the aligner. Ask if your cells are telling the truth—or if they're hiding something.