Amazing Science

The rational design of proteins with desired functions requires a comprehensive description of the functional building blocks. The evolutionary conserved functional units constitute nature's toolbox; however, they are not readily available to protein designers.

This study focuses on protein units of subdomain size that possess structural properties and amino acid residues sufficient to carry out elementary reactions in the catalytic mechanisms. The interactions within such elementary functional loops (ELFs) and the interactions with the surrounding protein scaffolds constitute the descriptor of elementary function. The computational approach to deriving descriptors directly from protein sequences and structures and applying them in rational design was implemented in a proof-of-concept DEFINED-PROTEINS software package. Once the descriptor is obtained, the ELF can be fitted into existing or novel scaffolds to obtain the desired function. For instance, the descriptor may be used to determine the necessary spatial restraints in a fragment-based grafting protocol.

The researchers illustrated the approach by applying it to well-known cases of ELFs, including phosphate-binding P-loop, diphosphate-binding glycine-rich motif, and calcium-binding EF-hand motif, which could be used to jumpstart templates for user applications.

The DEFINED-PROTEINS package is available for free at https://github.com/MelvinYin/Defined_Proteins.

Scientists have created an AI system capable of generating artificial enzymes from scratch. In laboratory tests, some of these enzymes worked as well as those found in nature, even when their artificially generated amino acid sequences diverged significantly from any known natural protein.

Three billion years of evolution have produced a tremendous diversity of proteins, and yet the full potential of this molecular class is likely far greater than we can even imagine. Accessing this potential has been challenging for computation and experiment because the space of possible protein molecules is much larger than the space that are likely to host function.

Now, a team of scientists introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, they were able to introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems based on random graph neural networks that enables long-range reasoning with sub-quadratic scaling, equivariant layers for efficiently synthesizing 3D structures of proteins from predicted inter-residue geometries, and a general low-temperature sampling algorithm for diffusion models. Importantly, Chroma can effectively realize protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics, and even natural language prompts. With this unified approach, we hope to accelerate the prospect of programming protein matter for human health, materials science, and synthetic biology.

Chroma learns patterns in the three-dimensional structures and amino acid sequences of proteins and protein complexes from the Protein Data Bank. By learning these patterns in a way that generalizes across natural proteins, Chroma can synthesize new protein molecules that adhere to these principles while combining them in novel ways. Importantly, Chroma can be conditioned on a set of desired structural or functional properties, such as the presence of functional structural motifs, symmetry constraints, adhering to a pre-specified shape, belonging to a domain or functional class, or even satisfying text-based descriptions. Thus, Chroma will enable a new, programmable mode of protein engineering where it is routine and feasible to generate specific and tailored protein solutions to complex challenges for bioengineering and human health.

The Chroma system is built on several new machine learning components, including a new neural network architecture for processing and manipulating 3D molecular information, a new diffusion process for adding noise to structures while adhering to the biophysical constraints of protein chains, and a new generalized method for generating high-quality samples from diffusion models. As a result of these innovations, Chroma is able to generate extremely large proteins and protein complexes (e.g. 30,000+ heavy atoms across 4,000+ residues) in a few minutes on a single commodity GPU.

The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult.

In this present work, a group of scientists present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. This approach describes protein backbone structure as a series of consecutive angles capturing the relative orientation of the constituent amino acid residues, and generate new structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this strategy mirror how proteins biologically twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for complex equivariant networks.

The team was successful in training a denoising diffusion probabilistic model with a simple transformer backbone and were able to demonstrate that the resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins.

As a useful and valuable resource, they released the first open-source codebase and trained models for protein structure diffusion to the scientific world.

Code: https://github.com/microsoft/foldingdiff…

Preprint: https://arxiv.org/abs/2209.15611

Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. Researchers now address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues.

Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. Sei is provided as a resource to elucidate the regulatory basis of human health and disease. Sei is a new framework for integrating human genetics data with a sequence-based mapping of predicted regulatory activities to elucidate mechanisms contributing to complex traits and diseases.

Deciphering how regulatory functions are encoded in genomic sequences is a major challenge in understanding how genome variation links to phenotypic traits. Cell type-specific regulatory activities encoded in elements such as promoters, enhancers and boundary elements are critical to defining the complex expression programs essential for multicellular organisms. Most disease-associated variants from genome-wide association studies (GWAS) are located in noncoding regions1, yet without knowing how changes in sequence affect regulatory activities, we cannot predict the impact of these variants and uncover the regulatory mechanisms contributing to complex diseases and traits.

Substantial progress has been made in the experimental profiling and integrative analysis of epigenomic marks, such as histone marks and DNA accessibility, across a wide range of tissues and cell types2,3,4. At the same time, deep learning sequence modeling techniques have been successfully applied to learn sequence features predictive of transcription factor (TF) binding and histone modifications5,6,7,8,9,10,11. These models are powerful tools for inferring the impact of sequence variation at the chromatin level—for example, whether a variant increases or decreases C/EBPβ binding. However, we continue to lack an integrative view of sequence regulatory activities, including all major aspects of cis-regulatory functions, such as tissue-specific or broad enhancer and promoter activities. This limits our ability to interpret the integrated effects of all chromatin-level perturbations caused by genomic variants and determine their impact on human health and diseases.

The researchers address this challenge by creating a global map for sequence regulatory activity based on a new deep learning-based framework called Sei. This framework introduces a sequence model that predicts 21,907 publicly available chromatin profiles—the broadest set to date—and uses the model to quantitatively characterize regulatory activities for any sequence with a vocabulary we call sequence classes. Sequence classes cover diverse types of regulatory activities, such as promoter or cell type-specific enhancer activity, across the whole genome by integrating sequence-based predictions from histone marks, TFs and chromatin accessibility across a wide range of cell types. Importantly, sequence classes can be used to both classify and quantify the regulatory activities of any sequence based on predictions made by the deep learning sequence model, thereby allowing any mutation to be quantified by its impact (for example, increase, decrease or no change) on cell type-specific regulatory activities.

Thus, Sei enables an interpretable and systematic integration of sequence-based regulatory activity predictions with human genetics data to elucidate the regulatory basis of complex traits and diseases. We applied our framework to characterize disease- and trait-associated regulatory disruptions in GWAS data based on a nonoverlapping partitioning of heritability by regulatory activities. Moreover, we applied variant effect prediction at the sequence class-level to interpret cell type-specific regulatory mechanisms for individual disease mutations and differentiate between gain-of-function (GoF) and loss-of-function (LoF) regulatory mutations.

The Sei framework in its current form is provided as a resource for systematically classifying and scoring any sequence and variant with sequence classes, additionally providing the Sei model predictions for the 21,907 chromatin profiles underlying the sequence classes. The framework can be run using the code available at https://github.com/FunctionLab/sei-framework; a user-friendly web server is available at hb.flatironinstitute.org/sei.

The VGP Genomes are among the most complete reference genomes for their species and will be stored and publicly available in the genome ark database, a digital open-access library of genomes.The Vertebrate Genomes Project (VGP), a project of the G10K Consortium, aims to generate near error-free reference genome assemblies of ~70,000 extant vertebrate species. The VGP Species List details our Phase 1 Progress and includes a full list of the >70,000 extant, vertebrate species.

These genomes will be used to address fundamental questions in biology and disease, to identify species most genetically at risk for extinction, and to preserve genetic information of life. The motivation for the VGP is based in part on the G10K mission to generate genomes of 10,000 or more vertebrate species and on lessons learned from The Avian Phylogenomics Project.

The VGP will be completed based on taxonomic hierarchy, which is a relative ranking of a group of organisms beginning with the largest classification, domain, to the smallest classification, species: Orders (Phase 1), Families (Phase 2), and Genera (Phase 3) to eventually all species (Phase 4). This strategy will allow us to gain scientific insight at each phase, to continue to integrate emerging technologies, and to complete genomic analyses at increasing levels of phylogenetic scale. Additionally, we expect our approaches and questions at each phase to lead to the development of new algorithms, including algorithms for genome assemblies, alignments, annotations, comparative genomics, etc., which would then be applied to the next phase. This approach will also help us secure the needed funds in stages through grants and other fundraising efforts.

At each phase, species selection is based on a combination of criteria, including those with existing draft genomes in need of improvement, those with specialized traits that inform us about human biology, those in immediate danger of becoming extinct, and those with prominent use in biomedical research. Endangered species are a high priority and critical because our planet is experiencing its 6th mass extinction event, the worst since the die-off of dinosaurs 66 million years ago. This priority is in part due to human influence on pollution, habitat destruction, and climate change. Our planet is at risk of losing 1 in 8 vertebrate species (~8000 in total) to extinction. The VGP aims to map the DNA of these at-risk species to not only preserve blueprints of their genomes but also to help identify genetic variants that might protect these species from total extinction. This knowledge can guide conservation strategies to prevent or at least minimize loss of species resulting from humankind’s impact on the environment.

The VGP intends to use the genomic data that it produces for multiple studies. A list of studies planned for the Phase 1 ordinal VGP include:

1. Genome-scale family tree of vertebrates.
2. Comparative genomics of specialized traits in each vertebrate lineage.
3. Comparative genomics of convergent traits (e.g. vocal learning, flight, loss of limbs, and aquatic / terrestrial adaptations).
4. Developing universal vertebrate gene orthology and nomenclature.
5. Deciphering vertebrate chromosomal genome evolution.
6. Reconstruction of the common ancestor genomes of all vertebrates and of key vertebrate clades (e.g. mammals, birds, reptiles, amphibians, teleost, bony vertebrates, jawed vertebrates, and tetrapods).
7. Evolution of nucleotides to chromosomes of the human genome.
8. Genetics of why some lineages are more disease resistant than others.
9. Conservation genomics of endangered species sequenced.
10. The genomes of all remaining Kakapo parrots on the planet.
11. Genetic signatures of domestication across vertebrates.
12. Genetics of sex determination and sex chromosome evolution among vertebrates.
13. Brain cell type evolution and homologies using genomics and transcriptomics.
14. 3-D genome structure across vertebrates.
15. Consequences of the evolutionary battle between transposons and host factors.
16. New algorithms for near complete genome assemblies.
17. New algorithms for reference free multi-way genome alignments.

The virus has mutated. But that doesn’t mean it’s getting deadlier.

At this point in the pandemic, coronavirus genomes with 10 or fewer mutations are common, and only a small number have over 20 mutations — which is still less than a tenth of a percent of the genome.

Over time, viruses can evolve into new strains — in other words, viral lineages that are significantly different from each other. Since January, researchers have sequenced many thousands of SARS-CoV-2 genomes and tracked all the mutations that have arisen. So far, they haven’t found compelling evidence that the mutations have had a significant change in how the virus affects us.

Crumble is a newly published NGS compression method by James Bonfield, Shane McCarthy, Richard Durbin of the Sanger Institute. Crumble provides size savings of 20-70% for BAM and CRAM files from HiSeq2500, HiSeqX, and NovaSeq Whole Genome Sequencing (WGS) and exomes. The resulting variant calls, showing generally similar results when using Crumble, even slightly improving variant-calling accuracy on the same sequencing data.

Among computational fields, genomics is especially data-heavy. For a 30x WGS sample, the size of the BAM file can easily exceed 100 GB. The collective size of genomics projects is growing at a tremendous rate. One highly cited work by Stephens et al. projects that, with current trends, the volume of NGS data in 2025 will exceed the data footprint of YouTube, Twitter, and astronomy. Most of the data footprint is in BAM files. In this format, the data footprint is divided between Read Names, the Sequence itself, Quality Values (QV) and Tags. The CRAM format uses lossless reference compression for the sequence, pushing the relative data footprint toward storing the quality values (see our Readshift blog for background on QV).

In an uncompressed (SAM) file, each quality value occupies a bin with a fixed amount of space. The BAM format compresses this data with a standard method similar to gzip. To simplify compression methods greatly, they identify recurring patterns within the data that can be used to pack elements together. More irregular data (data with higher entropy) is harder to compress.

This blog from Dropbox contains interesting examples on how compression methods can work. As Illumina has progressively increased instrument throughput, it has fought the growing friction in managing data footprint by decreasing the range of possible QV (HiSeq2500 – 40, HiSeqX – 8, NovaSeq – 4). The figure illustrate how packing a stream of QV pieces together is easier when there are fewer types of QVs and these types are more similar. These changes are generally synergistic with other advanced compression technologies such as Petagene.

Sharing algorithms can benefit healthcare research and society at large.

“Despite the documented advantages of the open sharing of code, to date, the practice has been extremely limited within the field of cancer drug prediction,” a group of Georgia Tech researchers write in a new PLOS ONE article. But they’re trying to change that.

Calling its project “a gauntlet,” the team wants other researchers to take, use, modify, and expand upon a machine learning platform it built to judge cancer treatment effectiveness using genetic data.

“We feed in genomic data. We use RNA expression data. Basically, we’re just looking for correlations,” John McDonald, PhD, told Healthcare Analytics News™.  McDonald is the director of Georgia Tech’s Integrated Cancer Research Center and one of the study’s lead authors. “We put expression data in and we match that to responses to chemotherapeutic drugs.”

He said his team based the algorithm off the NCI-60 cell lines, representing 9 types of cancer. The lines have been tested with more than 1,000 drugs, but researchers built predictive models for only 9 drugs so far, focusing on ovarian cancer.

“The algorithm goes through looking for correlations between various gene expression profiles, and then it will come out and say, ‘These are the most informative features in your gene expression data set in terms of correlating to the response to the drug,’” McDonald explained.

The algorithm assesses treatment accurately more than 80% of the time. He said they’re currently applying it to individual patient samples from Northside Hospital in Atlanta, Georgia.

McDonald stressed the potential for clinical significance in cases where patients fail therapy on the standard of care drugs, which for ovarian cancer are carboplatin and taxol. As many as 30% of patients don’t respond to them. “It’s basically a crapshoot at this point. There’s no logic as to which drug should be tried next,” McDonald said. He added that his algorithm could help suggest drugs for second-line therapies.

As an ambitious project to map all the cells in the human body gets officially under way, Aviv Regev, Sarah Teichmann and colleagues outline some key challenges.

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. This week, 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.

Researchers create a network of interacting proteins – or interactome – to aid drug discovery

Scientists at Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), and GSK are revealing the shared basis of diseases using a map of interacting human proteins. By helping to understand how biological processes affect human traits and diseases, this work will prioritise new targets for drug discovery and identify drug repurposing opportunities.

Proteinsdo most of the work in our cells and are made following blueprints encoded in genes. They are essential for the structure, function, and regulation of the body’s tissues, and are often the target of drugs or therapies. Genome Wide Association Studies (GWAS) help us establish the genetic basis of disease by linking specific genes to diseases. To determine how those genes contribute to disease, we need to understand the function of the proteins they encode and link specific biological processes to diseases.

In this new study, published in the journal Nature Genetics, researchers created a network of interacting proteins – or an interactome – combining evidence from different sources, including EMBL-EBI’s IntAct database, Reactome, and Signor.

Using this interactome, they identified groups of proteins interacting with genes that have been linked through GWAS to over 1,000 human traits from 21 therapeutic areas.

Microbial molecules from soil, seawater and human bodies are among the planet’s least understood proteins. The ESM Metagenomic Atlas database contains structure predictions for 617 million proteins.

When London-based Deep Mind unveiled predicted structures for some 220 million proteins this year, it covered nearly every protein from known organisms in DNA databases. Now, another tech giant is filling in the dark matter of our protein universe.

Researchers at Meta (formerly Facebook) have used artificial intelligence (AI) to predict the structures of some 600 million proteins from bacteria, viruses and other microbes that haven’t been characterized. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

“These are the structures we know the least about. These are incredibly mysterious proteins. I think they offer the potential for great insight into biology,” says Alexander Rives, the research lead for Meta AI’s protein team.

The team generated the predictions — described in a November 2022 preprint — using a ‘large language model’, a type of AI that are the basis for tools that can predict text from just a few letters or words.

Normally language models are trained on large volumes of text. To apply them to proteins, Rives and his colleagues fed them sequences to known proteins, which can be expressed by a chains of 20 different amino acids, each represented by a letter. The network then learned to ‘autocomplete’ proteins with a proportion of amino acids obscured.

Protein ‘autocomplete’
This training imbued the network with an intuitive understanding of protein sequences, which hold information about their shapes, says Rives. A second step — inspired by DeepMind’s pioneering protein structure AI AlphaFold — combines such insights with information about the relationships between known protein structures and sequences, to generate predicted structures from protein sequences.

Meta’s network, called ESMFold, isn’t quite as accurate as AlphaFold, Rives’ team reported earlier this summer2, but it is about 60 times faster at predicting structures, he says. “What this means is that we can scale structure prediction to much larger databases.”

As a test case, they decided to wield their model on a database of bulk-sequenced ‘metagenomic’ DNA from environmental sources including soil, seawater, the human gut, skin and other microbial habitats. The vast majority of the DNA entries — which encode potential proteins — come from organisms that have never been cultured and are unknown to science.

In total, the Meta team predicted the structures of more than 617 million proteins. The effort took just 2 weeks (AlphaFold can take minutes to generate a single prediction). The predictions are freely available for anyone to use, as is the code underlying the model, says Rives. Of these 617 million predictions, the model deemed more than one-third to be high quality, such that researchers can have confidence that the overall protein shape is correct and, in some cases, can discern finer atomic-level details. Millions of these structures are entirely novel, and unlike anything in databases of protein structures determined experimentally or in the AlphaFold database of predictions from known organisms.

A good chunk of the AlphaFold database is made of structures that are nearly identical to each other, and ‘metagenomic’ databases “should cover a large part of the previously unseen protein universe”, says Martin Steinegger, a computational biologist at Seoul National University. “There’s a big opportunity now to unravel more of the darkness.”

Sergey Ovchinnikov, an evolutionary biologist at Harvard University in Cambridge, Massachusetts, wonders about the hundreds of millions of predictions that ESMFold made with low-confidence. Some might lack a defined structure, at least in isolation, whereas others might be non-coding DNA mistaken as a protein-coding material. “It seems there is still more than half of protein space we know nothing about,” he says.

Like a scene out of "Invasion of the Body Snatchers," a virus infects a host and converts it into a factory for making more copies of itself. Now researchers have shown that a large group of viruses, including the influenza viruses and other serious pathogens, steal genetic signals from their hosts to expand their own genomes.

This finding is presented in a study published June 25th, 2020 in Cell. The cross-disciplinary collaborative study was led by researchers at the Global Health and Emerging Pathogens Institute at Icahn School of Medicine at Mount Sinai in New York, and at the MRC-University of Glasgow Centre for Virus Research in the UK.

The cross-disciplinary team of virologists looked at a large group of viruses known as segmented negative-strand RNA viruses (sNSVs), which include widespread and serious pathogens of humans, domesticated animals and plants, including the influenza viruses and Lassa virus (the cause of Lassa fever). They showed that, by stealing genetic signals from their hosts, viruses can produce a wealth of previously undetected proteins. The researchers labeled them as UFO (Upstream Frankenstein Open reading frame) proteins, as they are encoded by stitching together the host and viral sequences. There was no knowledge of the existence of these kinds of proteins prior to this study.

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”).

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, scientists have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, examples of the successful use of proteomics and phosphoproteomics data sets is reviewed in as well as their integration other omics data sets with the aim of precision medicine. The authors of this study discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. They finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available.

However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, scientists developed GUTCYC, a compendium of environmental pathway genome databases (ePGDBs) constructed from 418 assembled human microbiome datasets using METAPATHWAYS, enabling reproducible functional metagenomic annotation. They also generated metabolic network reconstructions for each metagenome using the PATHWAYTOOLS software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GUTCYC provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GUTCYC data products are searchable online, or may be downloaded and explored locally using METAPATHWAYS and PATHWAY TOOLS.