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Science Reveals More About The Secrets Of 'Super-Agers'

Science Reveals More About The Secrets Of 'Super-Agers' | Microbiology | Scoop.it
Aging well is a topic most people have a personal interest in—science certainly does. And it’s revealed some interesting findings in recent years, as long-term studies on “super agers” from across the globe have come in. Of the general population, about a third of people above the age of 90 have dementia, and another third have cognitive decline. But it’s the remaining group of healthy agers that’s so intriguing to researchers.

A couple of new studies presented at a recent American Association for the Advancement of Science meeting looked at people who live well as they age—often into their 90s or beyond. What’s peculiar, and encouraging, is that a lot of how we age has to do not with genetics but with our choices—how we live, physically and socially. And this means that more may be in our control than we think.

One of the new studies, “The 90+ Study,” as the name suggests, has tracked people in their 90s in just about every way possible for 15 years—physical exams, detailed analysis of their social lives and lifestyle habits, and multiple brain scans, before and (if the person died during the study) after death. The other study, on “Super Agers,” looked at people in their 80s, whose cognition and memory matches that of people decades younger.


One factor that played a big role in how a person aged was social interaction: People who lived longer had very close relationships over the years. This connection has been found in many studies on long-term health, the most famous of which was the 80-year Harvard study that found relationships were a key predictor of longevity. “There are brain benefits of having good friends,” said Super Ager study author Emily Rogalski at a press conference.

Another important factor in aging well was, interestingly, drinking alcohol: Those who drank a couple of glasses of wine or beer per day were more likely to live longer, compared to abstainers. “That’s been shown all over the world,” said 90+ Study author Claudia Kawas at the conference. ”I have no explanation for it, but I do firmly believe that modest drinking is associated with longevity.”

Happily, “modest” caffeine intake was also associated with living longer. “The sweet spot for caffeine was 200-400 milligrams a day,” said Kawas.”which, depending on whether you’re a Starbucks fan or an old-fashioned drinker, is about two cups of coffee probably.” People who took in this much from coffee or tea lived longer than people who consumed more or less caffeine.

Another factor was exercising regularly, which isn’t so surprising: People who got as little as 15 minutes per day had an advantage when it came to longevity, and the effect rose with 30 and 45 minutes/day. There was no huge benefit above that, so people who exercised for hours a day had no advantage over those who exercised for 45 minutes.

Via Dr. Stefan Gruenwald
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Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming

Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming | Microbiology | Scoop.it
Harnessing beneficial microbes presents a promising strategy to optimize plant growth and agricultural sustainability. Little is known to which extent and how specifically soil and plant microbiomes can be manipulated through different cropping practices. Here, we investigated soil and wheat root microbial communities in a cropping system experiment consisting of conventional and organic managements, both with different tillage intensities. While microbial richness was marginally affected, we found pronounced cropping effects on community composition, which were specific for the respective microbiomes. Soil bacterial communities were primarily structured by tillage, whereas soil fungal communities responded mainly to management type with additional effects by tillage. In roots, management type was also the driving factor for bacteria but not for fungi, which were generally determined by changes in tillage intensity. To quantify an “effect size” for microbiota manipulation, we found that about 10% of variation in microbial communities was explained by the tested cropping practices. Cropping sensitive microbes were taxonomically diverse, and they responded in guilds of taxa to the specific practices. These microbes also included frequent community members or members co-occurring with many other microbes in the community, suggesting that cropping practices may allow manipulation of influential community members. Understanding the abundance patterns of cropping sensitive microbes presents the basis towards developing microbiota management strategies for smart farming. For future targeted microbiota management—e.g., to foster certain microbes with specific agricultural practices—a next step will be to identify the functional traits of the cropping sensitive microbes.

Via Stéphane Hacquard
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Jonathan Lapleau's curator insight, January 20, 5:50 AM
Combining cropping practices and soil microbes management = smart farming. We are at the beginning of big changes in agriculture.
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Phandango: an interactive viewer for bacterial population genomics | Bioinformatics | Oxford Academic

Phandango: an interactive viewer for bacterial population genomics | Bioinformatics | Oxford Academic | Microbiology | Scoop.it
AbstractSummary. Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesizing and integrating different types of

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Mobius Assembly: A versatile Golden-Gate framework towards universal DNA assembly

Mobius Assembly: A versatile Golden-Gate framework towards universal DNA assembly | Microbiology | Scoop.it
Synthetic biology builds upon the foundation of engineering principles, prompting innovation and improvement in biotechnology via a design-build-test-learn cycle. A community-wide standard in DNA assembly would enable bio-molecular engineering at the levels of predictivity and universality in design and construction that are comparable to other engineering fields. Golden Gate Assembly technology, with its robust capability to unidirectionally assemble numerous DNA fragments in a one-tube reaction, has the potential to deliver a universal standard framework for DNA assembly. While current Golden Gate Assembly frameworks (e.g. MoClo and Golden Braid) render either high cloning capacity or vector toolkit simplicity, the technology can be made more versatile—simple, streamlined, and cost/labor-efficient, without compromising capacity. Here we report the development of a new Golden Gate Assembly framework named Mobius Assembly, which combines vector toolkit simplicity with high cloning capacity. It is based on a two-level, hierarchical approach and utilizes a low-frequency cutter to reduce domestication requirements. Mobius Assembly embraces the standard overhang designs designated by MoClo, Golden Braid, and Phytobricks and is largely compatible with already available Golden Gate part libraries. In addition, dropout cassettes encoding chromogenic proteins were implemented for cost-free visible cloning screening that color-code different cloning levels. As proofs of concept, we have successfully assembled up to 16 transcriptional units of various pigmentation genes in both operon and multigene arrangements. Taken together, Mobius Assembly delivers enhanced versatility and efficiency in DNA assembly, facilitating improved standardization and automation.

Via Gerd Moe-Behrens
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Jonathan Lapleau's curator insight, February 1, 6:23 AM
Mobius Assembly : A Versatile Golden-Gate Framework Towards Universal DNA Assembly
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Google has released a machine learning AI tool that makes sense of your genome

Google has released a machine learning AI tool that makes sense of your genome | Microbiology | Scoop.it

AI tools could help us turn information gleaned from genetic sequencing into life-saving therapies. Almost 15 years after scientists first sequenced the human genome, making sense of the enormous amount of data that encodes human life remains a formidable challenge. But it is also precisely the sort of problem that machine learning excels at.

 

Google has now released a tool called DeepVariant that uses the latest AI techniques to build a more accurate picture of a person’s genome from sequencing data. DeepVariant helps turn high-throughput sequencing readouts into a picture of a full genome. It automatically identifies small insertion and deletion mutations and single-base-pair mutations in sequencing data.

 

High-throughput sequencing became widely available in the 2000s and has made genome sequencing more accessible. But the data produced using such systems has offered only a limited, error-prone snapshot of a full genome. It is typically challenging for scientists to distinguish small mutations from random errors generated during the sequencing process, especially in repetitive portions of a genome. These mutations may be directly relevant to diseases such as cancer.

 

A number of tools exist for interpreting these readouts, including GATK, VarDict, and FreeBayes. However, these software programs typically use simpler statistical and machine-learning approaches to identifying mutations by attempting to rule out read errors. “One of the challenges is in difficult parts of the genome, where each of the tools has strengths and weaknesses,” says Brad Chapman, a research scientist at Harvard’s School of Public Health who tested an early version of DeepVariant. “These difficult regions are increasingly important for clinical sequencing, and it’s important to have multiple methods.”

 

DeepVariant was developed by researchers from the Google Brain team, a group that focuses on developing and applying AI techniques, and Verily, another Alphabet subsidiary that is focused on the life sciences. The team collected millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle (GIAB)  project, a public-private effort to promote genomic sequencing tools and techniques. They fed the data to a deep-learning system and painstakingly tweaked the parameters of the model until it learned to interpret sequenced data with a high level of accuracy.

 

Last year, DeepVariant won first place in the PrecisionFDA Truth Challenge, a contest run by the FDA to promote more accurate genetic sequencing. “The success of DeepVariant is important because it demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems,” says Brendan Frey, CEO of Deep Genomics.

 

The release of DeepVariant is the latest sign that machine learning may be poised to boost progress in genomics. Deep Genomics is one of several companies trying to use AI approaches such as deep learning to tease out genetic causes of diseases and to identify potential drug therapies (see “An AI-Driven Genomics Company Is Turning to Drugs”).

 

Deep Genomics aims to develop drugs by using deep learning to find patterns in genomic and medical data. Frey says AI will eventually go well beyond helping to sequence genomic data. “The gap that is currently blocking medicine right now is in our inability to accurately map genetic variants to disease mechanisms and to use that knowledge to rapidly identify life-saving therapies,” he says.

 

Another prominent company in this area is Wuxi Nextcode, which has offices in Shanghai, Reykjavik, and Cambridge, Massachusetts. Wuxi Nextcode has amassed the world’s largest collection of fully sequenced human genomes, and the company is investing heavily in machine-learning methods.

 

DeepVariant will also be available on the Google Cloud Platform. Google and its competitors are furiously adding machine-learning features to their cloud platforms in an effort to lure anyone who might want to tap into the latest AI techniques (see “Ambient AI Is About to Devour the Software Industry”).

 

In general, AI figures to help many aspects of medicine take big leaps forward in the coming years. There are opportunities to mine many different kinds of medical data—from images or medical records, for example— to predict ailments that a human doctor might miss (see “The Machines Are Getting Ready to Play Doctor” and “A New Algorithm for Palliative Care”).


Via Dr. Stefan Gruenwald
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Art Jones's curator insight, December 7, 2017 9:24 PM

Google turns the sci-fi which amazed us on the big screen yesterday into today's reality. 

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Can data storage in DNA solve our massive data storage problem in the future?

Can data storage in DNA solve our massive data storage problem in the future? | Microbiology | Scoop.it

The latest in high-density ultra-durable data storage has been perfected over billions of years by nature itself.

 

Now ‘Smoke on the Water’ is making history again. This September, it was one of the first items from the Memory Of the World archive to be stored in the form of DNA and then played back with 100% accuracy. The project was a joint effort between the University of Washington, Microsoft and Twist Bioscience, a San Francisco-based DNA manufacturing company.

The demonstration was billed as a ‘proof of principle’ – which is shorthand for successful but too expensive to be practical. At least for now.

 

Many pundits predict it’s just a matter of time till DNA pips magnetic tape as the ultimate way to store data. It’s compact, efficient and resilient. After all, it has been tweaked over billions of years into the perfect repository for genetic information. It will never become obsolete, because as long as there is life on Earth, we will be interested in decoding DNA. “Nature has optimised the format,” says Twist Bioscience’s chief technology officer Bill Peck.

 

Players like Microsoft, IBM and Intel are showing signs of interest. In April, they joined other industry, academic and government experts at an invitation-only workshop (cosponsored by the U.S. Intelligence Advanced Research Projects Activity (IARPA)) to discuss the practical potential for DNA to solve humanity’s looming data storage crisis.

 

It’s a big problem that’s getting bigger by the minute. According to a 2016 IBM Marketing Cloud report, 90% of the data that exists today was created in just the past two years. Every day, we generate another 2.5 quintillion (2.5 × 1018) bytes of information. It pours in from high definition video and photos, Big Data from particle physics, genomic sequencing, space probes, satellites, and remote sensing; from think tanks, covert surveillance operations, and Internet tracking algorithms. EVERY DAY, WE GENERATE ANOTHER 2.5 QUINTILLION BYTES OF INFORMATION.

 

Right now all those bits and bytes flow into gigantic server farms, onto spinning hard drives or reels of state-of-the-art magnetic tape. These physical substrates occupy a lot of space. Compare this to DNA. The entire human genome, a code of three billion DNA base pairs, or in data speak, 3,000 megabytes, fits into a package that is invisible to the naked eye – the cell’s nucleus. A gram of DNA — the size of a drop of water on your fingertip — can store at least the equivalent of 233 computer hard drives weighing more than 150 kilograms. To store the all the genetic information in a human body — 150 zettabytes — on tape or hard drives, you’d need a facility covering thousands, if not millions of square feet.

 

And then there’s durability. Of the current storage contenders, magnetic tape has the best lifespan, at about 10-20 years. Hard drives, CDs, DVDs and flash drives are less reliable, often failing within five to ten years. DNA has proven that it can survive thousands of years unscathed. In 2013, for example, the genome of an early horse relative was reconstructed from DNA from a 700,000-year-old bone fragment found in the Alaskan permafrost.


Via Integrated DNA Technologies, Dr. Stefan Gruenwald
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Molecular tools for gene manipulation in filamentous fungi - App. Microbiology and Biotechnology

Molecular tools for gene manipulation in filamentous fungi - App. Microbiology and Biotechnology | Microbiology | Scoop.it
Functional genomics of filamentous fungi has gradually uncovered gene information for constructing ‘cell factories’ and controlling pathogens. Available gene manipulation methods of filamentous fungi include random integration methods, gene targeting technology, gene editing with artificial nucleases and RNA technology. This review describes random gene integration constructed by restriction enzyme-mediated integration (REMI); Agrobacterium-mediated transformation (AMT); transposon-arrayed gene knockout (TAGKO); gene targeting technology, mainly about homologous recombination; and modern gene editing strategies containing transcription activator-like effector nucleases (TALENs) and a clustered regularly interspaced short palindromic repeat/associated protein system (CRISPR/Cas) developed in filamentous fungi and RNA technology including RNA interference (RNAi) and ribozymes. This review describes historical and modern gene manipulation methods in filamentous fungi and presents the molecular tools available to researchers investigating filamentous fungi. The biggest difference of this review from the previous ones is the addition of successful application and details of the promising gene editing tool CRISPR/Cas9 system in filamentous fungi.

Via Ronny Kellner
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Life’s First Molecule Was Protein, Not RNA, New Model Suggests 

Life’s First Molecule Was Protein, Not RNA, New Model Suggests  | Microbiology | Scoop.it

Which mattered first at the dawn of life: proteins or nucleic acids? Proteins may have had the edge if a theorized process let them grow long enough to become self-replicating catalysts. 


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Kenzibit's curator insight, November 4, 2017 9:34 AM
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Refined DNA tool tracks native and invasive fish 

Refined DNA tool tracks native and invasive fish  | Microbiology | Scoop.it

Rather than conduct an aquatic roll call with nets to know which fish reside in a particular body of water, scientists can now use DNA fragments suspended in water to catalog invasive or native species.


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16S rRNA indexed primers amplify phylogenic markers for microbiome sequencing analysis

16S rRNA indexed primers amplify phylogenic markers for microbiome sequencing analysis | Microbiology | Scoop.it

The 16S rRNA gene is frequently used in microbiome studies to identify the subset of microbes present in biological samples. Researchers amplify short hypervariable regions from this gene, tag the amplified products with unique barcodes, perform highly multiplexed sequencing runs, and compare the sequences to the known bacterial genome database. However, primer design for such analyses can be challenging given the massive sequence variability in sampled lifeforms. Read about the development and design of these primers, and how you can obtain your own custom, high fidelity versions of these sequences.


Via Integrated DNA Technologies, Bwana Moses
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Paul Epping's curator insight, July 8, 2017 3:02 AM

An important step in the fight against resistant bacteria.

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Genome Spot

Genome Spot | Microbiology | Scoop.it
Practical tips for genome analysis

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Genome Partitioner: A web tool for multi-level partitioning of large-scale DNA constructs for synthetic biology applications

Recent advances in lower-cost DNA synthesis techniques have enabled new innovations in the field of synthetic biology. Still, efficient design and higher-order assembly of genome-scale DNA constructs remains a labor-intensive process. Given the complexity, computer assisted design tools that fragment large DNA sequences into fabricable DNA blocks are needed to pave the way towards streamlined assembly of biological systems. Here, we present the Genome Partitioner software implemented as a web-based interface that permits multi-level partitioning of genome-scale DNA designs. Without the need for specialized computing skills, biologists can submit their DNA designs to a fully automated pipeline that generates the optimal retrosynthetic route for higher-order DNA assembly. To test the algorithm, we partitioned a 783 kb Caulobacter crescentus genome design. We validated the partitioning strategy by assembling a 20 kb test segment encompassing a difficult to synthesize DNA sequence. Successful assembly from 1 kb subblocks into the 20 kb segment highlights the effectiveness of the Genome Partitioner for reducing synthesis costs and timelines for higher-order DNA assembly. The Genome Partitioner is broadly applicable to translate DNA designs into ready to order sequences that can be assembled with standardized protocols, thus offering new opportunities to harness the diversity of microbial genomes for synthetic biology applications. The Genome Partitioner web tool can be accessed at https://christenlab.ethz.ch/GenomePartitioner.

Via Gerd Moe-Behrens
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UNSW-Harvard scientists unveil a giant leap for anti-ageing

UNSW-Harvard scientists unveil a giant leap for anti-ageing | Microbiology | Scoop.it

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Viruses - lots of them - are falling from the sky

Viruses - lots of them - are falling from the sky | Microbiology | Scoop.it

An astonishing number of viruses are circulating around the Earth's atmosphere -- and falling from it -- according to new research from scientists in Canada, Spain and the U.S.

 

The study marks the first time scientists have quantified the viruses being swept up from the Earth's surface into the free troposphere, that layer of atmosphere beyond Earth's weather systems but below the stratosphere where jet airplanes fly. The viruses can be carried thousands of kilometers there before being deposited back onto the Earth's surface.

 

"Every day, more than 800 million viruses are deposited per square metre above the planetary boundary layer -- that's 25 viruses for each person in Canada," said University of British Columbia virologist Curtis Suttle, one of the senior authors of a paper in the International Society for Microbial Ecology Journal that outlines the findings.

 

"Roughly 20 years ago we began finding genetically similar viruses occurring in very different environments around the globe," says Suttle. "This preponderance of long-residence viruses traveling the atmosphere likely explains why -- it's quite conceivable to have a virus swept up into the atmosphere on one continent and deposited on another."

 

Bacteria and viruses are swept up in the atmosphere in small particles from soil-dust and sea spray. Suttle and colleagues at the University of Granada and San Diego State University wanted to know how much of that material is carried up above the atmospheric boundary layer above 2,500 to 3,000 meters. At that altitude, particles are subject to long-range transport unlike particles lower in the atmosphere.

 

Using platform sites high in Spain's Sierra Nevada Mountains, the researchers found billions of viruses and tens of millions of bacteria are being deposited per square meter per day. The deposition rates for viruses were nine to 461 times greater than the rates for bacteria.


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Ten simple rules for biologists learning to program

Ten simple rules for biologists learning to program | Microbiology | Scoop.it

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ReMILO: reference assisted misassembly detection algorithm using short and long reads | Bioinformatics | Oxford Academic

ReMILO: reference assisted misassembly detection algorithm using short and long reads | Bioinformatics | Oxford Academic | Microbiology | Scoop.it
AbstractMotivation. Contigs assembled from the second generation sequencing short reads may contain misassemblies, and thus complicate downstream analysis or e

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Phandango: an interactive viewer for bacterial population genomics | Bioinformatics | Oxford Academic

Phandango: an interactive viewer for bacterial population genomics | Bioinformatics | Oxford Academic | Microbiology | Scoop.it
AbstractSummary. Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesizing and integrating different types of

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The Human Cell Atlas: an ambitious project to map all the cells in the human body gets officially under way

The Human Cell Atlas: an ambitious project to map all the cells in the human body gets officially under way | Microbiology | Scoop.it

Our knowledge of the cells that make up the human body, and how they vary from person to person, or throughout development and in health or disease, is still very limited. Recently, a year after project planning began, more than 130 biologists, computational scientists, technologists and clinicians are reconvening in Rehovot, Israel, to kick the Human Cell Atlas initiative1 into full gear. This international collaboration between hundreds of scientists from dozens of universities and institutes — including the UK Wellcome Trust Sanger Institute, RIKEN in Japan, the Karolinska Institute in Stockholm and the Broad Institute of MIT and Harvard in Cambridge, Massachusetts — aims to create comprehensive reference maps of all human cells as a basis for research, diagnosis, monitoring and treatment.

 

On behalf of the Human Cell Atlas organizing committee, we outline here some of the key challenges faced in building such an atlas — and our proposed strategies. For more details on how the atlas will be built as an open global resource, see the white paper2 posted on the Human Cell Atlas website.

 

Cells have been characterized and classified with increasing precision since Robert Hooke first identified them under the microscope in the seventeenth century. But biologists have not yet determined all the molecular constituents of cells, nor have they established how all these constituents are associated with each other in tissues, systems and organs. As a result, there are many cell types we don’t know about. We also don’t know how all the cells in the body change from one state to another, which other cells they interact with or how they are altered during development.

Technology revolution 

New technologies offer an opportunity to build a systematic atlas at unprecedented resolution. These tools range from single-cell RNA sequencing to techniques for assessing a cell’s protein molecules and profiling the accessibility of the chromatin. For example, we can now determine the RNA profiles for millions of individual cells in parallel (see ‘From one to millions’). Protein composition and chromatin features can be studied in hundreds or thousands of individual cells, and mutations or other markers tracked to reconstruct cell lineages. We can also profile multiple variants of RNA and proteins in situ to map cells and their molecules to their locations in tissues.


Via Dr. Stefan Gruenwald
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Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities | Microbiology | Scoop.it

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.


Via Stéphane Hacquard, Ronny Kellner
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Synima: a Synteny imaging tool for annotated genome assemblies - BMC Bioinformatics

Synima: a Synteny imaging tool for annotated genome assemblies - BMC Bioinformatics | Microbiology | Scoop.it

Background
Ortholog prediction and synteny visualization across whole genomes are valuable methods for detecting and representing a range of evolutionary processes such as genome expansion, chromosomal rearrangement, and chromosomal translocation. Few standalone methods are currently available to visualize synteny across any number of annotated genomes.

Results
Here, I present a Synteny Imaging tool (Synima) written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima – and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from https://github.com/rhysf/Synima under the MIT License.

Conclusions
Synima should be a valuable tool for visualizing synteny between two or more annotated genome assemblies.


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Acetabularia alga can grow to 10 cm (4 inches) and is a single cell

Acetabularia alga can grow to 10 cm (4 inches) and is a single cell | Microbiology | Scoop.it

If I asked you what was the experimental basis for the central dogma of biology (DNA makes RNA makes Protein), you would be likely to mention the classical findings that the transforming principle was DNA (Avery et al.) or that phages transfer DNA to the host (Hershey & Chase). However, it is unlikely that you even have heard that the precept was earlier derived from studies with a unicellular marine alga, Acetabularia. If so, you would miss the remarkable biology that made it possible to carry out this work. Here is why: Acetabularia is such a large cell that it can be readily handled with one's hands.  It can be amputated into pieces that can be grafted together and its nucleus transplanted as easily as walking in the park.

 

Most cells are clearly too small for such luxuries. To enjoy them, we must turn to the outliers in range of sizes, that is, to giant cells. So, how big can cells get? The champion seems to be another a marine alga,Caulerpa, which can reach 3 meters in length. It is multinucleated, which seems almost like cheating (consider acellular slime molds, which can also reach enormous sizes, and othercoenocytic organisms). Incidentally, Caulerpas are edible and are called sea grapes in Okinawa (海葡萄 or umi-budō). Also multinucleated are the xenophyophores, foraminifera-like protists that live in the ocean at depths below 500 meters and reach 15 cm across (and which were mentionedhere earlier). Among the largest uninucleated single cells are the foraminifera called Nummulites, which can reach 5 cm in diameter, and a marine ameba called Gromia spherica.


Via Dr. Stefan Gruenwald
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Salk scientists solve longstanding biological mystery of DNA organization 

Salk scientists solve longstanding biological mystery of DNA organization  | Microbiology | Scoop.it

Researchers image 3D genome in nucleus of living human cell for the first time


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Gut bacterial peptides with autoimmunity potential as environmental trigger for late onset complex diseases: In–silico study

Gut bacterial peptides with autoimmunity potential as environmental trigger for late onset complex diseases: In–silico study | Microbiology | Scoop.it
Recent evidences suggest that human gut microbiota with major component as bacteria can induce immunity. It is also known that gut lining depletes with ageing and that there is increased risk of autoimmune and inflammatory disorders with ageing. It is therefore likely that both may be correlated as depletion of gut lining exposes the gut bacterial antigens to host immune mechanisms, which may induce immunity to certain bacterial proteins, but at the same time such immunity may also be auto-immunogenic to host. This autoimmunity may make a protein molecule nonfunctional and thereby may be involved in late onset metabolic, autoimmune and inflammatory disorders such as, Diabetes, Rheumatoid Arthritis, Hyperlipidemias and Cancer. In this in-silico study we found a large number of peptides identical between human and gut bacteria which were binding to HLA-II alleles, and hence, likely to be auto-immunogenic. Further we observed that such autoimmune candidates were enriched in bacterial species belonging to Firmicutes and Proteobacteria phyla, which lead us to conclude that these phyla may have higher disease impact in genetically predisposed individuals. Functional annotation of human proteins homologous to candidate gut-bacterial peptides showed significant enrichment in metabolic processes and pathways. Cognitive trait, Ageing, Alzheimer, Type 2 diabetes, Chronic Kidney Failure (CKF), Chronic Obstructive Pulmonary Disease (COPD) and various Cancers were the major diseases represented in the dataset. This dataset provides us with gut bacterial autoimmune candidates which can be studied for their clinical significance in late onset diseases.

Via Krishan Maggon , Gilbert C FAURE, Kenzibit
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MUHAMMAD HAMDI BIN MAT SAAD's curator insight, February 20, 12:47 AM

Found this amazing article about how gut bacterial, which is proteobacteria as environmental trigger for late onset complex diseases. NICE!!!

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Bioinformatics

Bioinformatics | Microbiology | Scoop.it
A place for all Bioinformatics resources: Databases, Tools, Softwares, Books, Blogs, etc.

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Rescooped by Khashayar Farrokhzad from Viruses, Immunology & Bioinformatics from Virology.uvic.ca
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Staying Current in Bioinformatics & Genomics: 2017 Edition

Staying Current in Bioinformatics & Genomics: 2017 Edition | Microbiology | Scoop.it

Stephen Turner is an assistant professor of public health sciences and director of the Bioinformatics Core at the University of Virginia.

 

It has taken Stephen almost a decade to compile and continually hone this list of resources to the things that are useful and relevant. This is what works for him, now, in 2017. It’s not a one-size-fits-all list, and 2018 will probably have a somewhat different list, but Stephen hopes you’ll find the current list useful.


Via Dr. Stefan Gruenwald, Chris Upton + helpers
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