The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

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Initial sequencing and analysis of the human genome.

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.

https://www.cs.utexas.edu/~tandy/velvet.pdf

Velvet: Algorithms for de novo short read assembly using de Bruijn graphs

We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.

http://science.sciencemag.org/content/sci/298/5600/1912.full.pdf

The Protein Kinase Complement of the Human Genome

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

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Correction: Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients

Most current tumor immunotherapy strategies leverage cytotoxic CD8+ T cells. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their activity. To address this issue, we have used single-cell mRNA sequencing to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). Designing new computational approaches to characterize subpopulations, we identify TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes follicular helper (Tfh)-like cells but lacks Th1 cells. We identify a type I interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor specific CD4+ T cell effector programs.
Targeting these programs should help improve immunotherapy strategies. One Sentence Summary Single-cell RNA sequencing reveals novel and highly diverse transcriptomic patterns characteristic of CD4+ T cell responses to tumors.

https://biorxiv.org/content/biorxiv/early/2019/02/08/543199.full-text.pdf

Single-cell profiling of tumor-reactive CD4+ T-cells reveals unexpected transcriptomic diversity

Intratumoral heterogeneity (ITH) can promote cancer progression and treatment failure, but the complexity of the regulatory programs and contextual factors involved complicates its study. To understand the specific contribution of ITH to immune checkpoint blockade (ICB) response, we generated single cell-derived clonal sublines from an ICB-sensitive and genetically and phenotypically heterogeneous mouse melanoma model, M4. Genomic and single cell transcriptomic analyses uncovered the diversity of the sublines and evidenced their plasticity. Moreover, a wide range of tumor growth kinetics were observed in vivo, in part associated with mutational profiles and dependent on T cell-response. Further inquiry into melanoma differentiation states and tumor microenvironment (TME) subtypes of untreated tumors from the clonal sublines demonstrated correlations between highly inflamed and differentiated phenotypes with the response to anti-CTLA-4 treatment. Our results demonstrate that M4 sublines generate intratumoral heterogeneity at both levels of intrinsic differentiation status and extrinsic TME profiles, thereby impacting tumor evolution during therapeutic treatment. These clonal sublines proved to be a valuable resource to study the complex determinants of response to ICB, and specifically the role of melanoma plasticity in immune evasion mechanisms.

/pdf/melanoma-clonal-subline-analysis-uncovers-heterogeneity-3pwa8u1y.pdf

Melanoma clonal subline analysis uncovers heterogeneity-driven immunotherapy resistance mechanisms

Materials and methods Using P300-bound regions as putative enhancers [1] and using cell-type-specific DNAse hypersensitivity (HS) at these enhancers as an operational definition of enhancer activity, here we perform a detailed investigation of enhancer activity correlation across 15 cell types, followed by analysis of mechanistic underpinnings and functional consequences of correlated enhancer activity. We initially identify pairs of highly correlated enhancers from the same (cis) and from different chromosomes, after accounting for HS autocorrelation affecting cis-pairs. Using nonparametric tests and controlling for dependencies, highly correlated pairs are compared with background pairs for: enrichment of co-occurring binding motifs; for correlated gene expression across the 15 cell samples sourced for HS data; for shared gene function; for evidence of interactions between shared enhancer-binding transcription factors (TFs) and chromatin-modifying enzymes; and for Hi-C evidence of pair co-localization. The relationship between correlated enhancers now established, we conclude by scaling this pairs perspective to the building and validating of an enhancer network.

/pdf/enhancer-network-revealed-by-correlated-dnase-hs-states-of-79f8ig306w.pdf

Enhancer network revealed by correlated DNAse HS states of enhancers

Simple Summary Cancer initiation and progression has been studied in purely genetic terms for decades. Implicit in this view is that all cells in a tissue are phenotypically identical until a single cell acquires a set of mutations that will eventually lead to malignancy. This model does not fully account for numerous clinical and epidemiological findings about cancer incidence and progression. Here, we summarize recent mathematical and biological insights that demonstrate how cells in a tissue can switch between dramatically different phenotypes independent of mutations. We explore how these insights, combined with our detailed understanding of oncogenic mutations, may answer key unexplained aspects of pancreatic ductal adenocarcinoma initiation. Importantly, such a combined model allows for a more nuanced understanding of pre-malignancy, and points the way towards early detection and intervention approaches in high-risk patients. Abstract While much of the research in oncogenesis and cancer therapy has focused on mutations in key cancer driver genes, more recent work suggests a complementary non-genetic paradigm. This paradigm focuses on how transcriptional and phenotypic heterogeneity, even in clonally derived cells, can create sub-populations associated with oncogenesis, metastasis, and therapy resistance. We discuss this complementary paradigm in the context of pancreatic ductal adenocarcinoma. A better understanding of cellular transcriptional heterogeneity and its association with oncogenesis can lead to more effective therapies that prevent tumor initiation and slow progression.

https://www.mdpi.com/2072-6694/15/7/2159/pdf?version=1680697154

Sridhar Hannenhalli

Papers

Correction: Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients

Single-cell profiling of tumor-reactive CD4+ T-cells reveals unexpected transcriptomic diversity

Melanoma clonal subline analysis uncovers heterogeneity-driven immunotherapy resistance mechanisms

Enhancer network revealed by correlated DNAse HS states of enhancers

Towards a Synthesis of the Non-Genetic and Genetic Views of Cancer in Understanding Pancreatic Ductal Adenocarcinoma Initiation and Prevention