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

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

https://cdr.lib.unc.edu/downloads/v692t8920

A global reference for human genetic variation.

Comprehensive Integration of Single-Cell Data.

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

/pdf/finding-the-missing-heritability-of-complex-diseases-3b6mbqztnq.pdf

Finding the missing heritability of complex diseases

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

/pdf/an-integrated-map-of-genetic-variation-from-1-092-human-4vxat9g1wd.pdf

An integrated map of genetic variation from 1,092 human genomes

Provided herein are methods and compositions for placing single target molecules on a patterned substrate.

Compositions and methods for single molecular placement on a substrate

Embodiments of the present disclosure include methods and compositions for functionalizing molecules, such as oligonucleotides, with functional groups, including polyhistidine tags useful in affinity methods. Some embodiments include methods for modifying and purifying complex mixtures of molecules by exchange of functional tags.

Functionalization and purification of molecules by reversible group exchange

In one aspect, a detection apparatus comprises a solid support with an array of solid-state nanopores and a plurality of lipid nanodiscs with protein nanopores, wherein the lipid nanodiscs are disposed on the solid support such that they form seals at the solid-state nanopores. In another aspect, a detection apparatus comprises one or more nanopores that are surrounded by a membrane, wherein each nanopore is tethered to an electrode.

Hybrid nanopore sensors

The present disclosure relates to methods and kits for analysis of polypeptides. In some embodiments, the present methods and kits employ barcoding and nucleic acid encoding of molecular recognition events, and/or detectable labels.

Methods and compositions for polypeptide analysis

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC

Millions of Formalin Fixed Paraffin Embedded (FFPE) cancer tissue samples currently present in the US and worldwide provide an enormous, invaluable repository to the discovery of biomarkers in cancer research, drug development, diagnosis and treatment of diseases. However, the processes of fixation and storage of FFPE samples lead to degradation and base modification with chemical residues. This degraded FFPE DNA is inefficiently amplified in the Infinium® whole genome amplification (WGA) step leading to poor performance in the Infinium® assay.

In this study, FFPE DNA (100 ng input) was restored through a treatment with DNA polymerase, DNA repair enzyme, ligase, and modified Infinium® WGA reaction conditions. Canonical genotype cluster files were generated by creating artificially degraded Coriell DNAs and processing the samples through the restoration process and Infinium® assay. Results of genotyping on high density HumanCytoSNP-12 BeadChip®, featuring approximately 300k SNPs indicated that even highly degraded samples which show initial call rates of 40-70% can be successfully restored to a state that achieved call rates of greater than 90%, at a concordance greater than 99.0%, and associated standard deviation of log intensity less than 0.4. In general, over 80% of FFPE samples, which passed a QC assay metric were successfully restored and genotyped. Infinium® genotyping of restored DNA from a collection of tumor FFPE samples confirmed the presence of genomic aberrations around known cancer genes. Finally, we further demonstrated that paired sample analysis using canonical genotype clusters provide improved precision in CNV and LOH analysis compared to single sample analysis.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-134.

Kevin L. Gunderson

Papers

Compositions and methods for single molecular placement on a substrate

Functionalization and purification of molecules by reversible group exchange

Hybrid nanopore sensors

Methods and compositions for polypeptide analysis

Abstract LB-134: Analysis of restored FFPE samples on high-density SNP arrays