The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

In the past six years worldwide capacity for human genome sequencing has grown by more than five orders of magnitude, with costs falling by nearly two orders of magnitude over the same period [1], [2]. The rapid expansion in the production of next-generation sequence data and the use of these data in a wide range of new applications has created a need for improved computational tools for data processing. The Sentieon Genomics tools provide an optimized reimplementation of the most accurate pipelines for calling variants from next-generation sequence data, resulting in more than a 10-fold increase in processing speed while providing identical results to best practices pipelines. Here we demonstrate the consistency and improved performance of Sentieon’s tools relative to BWA, GATK, MuTect, and MuTect2 through analysis of publicly available human exome, low-coverage genome, and high-depth genome sequence data.

Neuropsychiatric disorders have a complex genetic architecture. Human genetic population-based studies have identified numerous heritable sequence and structural genomic variants associated with susceptibility to neuropsychiatric disease. However, these germline variants do not fully account for disease risk. During brain development, progenitor cells undergo billions of cell divisions to generate the ~80 billion neurons in the brain. The failure to accurately repair DNA damage arising during replication, transcription, and cellular metabolism amid this dramatic cellular expansion can lead to somatic mutations. Somatic mutations that alter subsets of neuronal transcriptomes and proteomes can, in turn, affect cell proliferation and survival and lead to neurodevelopmental disorders. The long life span of individual neurons and the direct relationship between neural circuits and behavior suggest that somatic mutations in small populations of neurons can significantly affect individual neurodevelopment. The Brain Somatic Mosaicism Network has been founded to study somatic mosaicism both in neurotypical human brains and in the context of complex neuropsychiatric disorders.

Somatic mosaicism refers to the occurrence of two genetically distinct populations of cells within an individual, derived from a postzygotic mutation. In contrast to inherited mutations, somatic mosaic mutations may affect only a portion of the body and are not transmitted to progeny. These mutations affect varying genomic sizes ranging from single nucleotides to entire chromosomes and have been implicated in disease, most prominently cancer. The phenotypic consequences of somatic mosaicism are dependent upon many factors including the developmental time at which the mutation occurs, the areas of the body that are affected, and the pathophysiological effect(s) of the mutation. The advent of second-generation sequencing technologies has augmented existing array-based and cytogenetic approaches for the identification of somatic mutations. We outline the strengths and weaknesses of these techniques and highlight recent insights into the role of somatic mosaicism in causing cancer, neurodegenerative, monogenic, and complex disease.

De novo mutation is highly implicated in autism spectrum disorder (ASD). However, the contribution of post-zygotic mutation to ASD is poorly characterized. We performed both exome sequencing of paired samples and analysis of de novo variants from whole-exome sequencing of 2,388 families. While we find little evidence for tissue-specific mosaic mutation, multi-tissue post-zygotic mutation (i.e. mosaicism) is frequent, with detectable mosaic variation comprising 5.4% of all de novo mutations. We identify three mosaic missense and likely-gene disrupting mutations in genes previously implicated in ASD (KMT2C, NCKAP1, and MYH10) in probands but none in siblings. We find a strong ascertainment bias for mosaic mutations in probands relative to their unaffected siblings (p = 0.003). We build a model of de novo variation incorporating mosaic variants and errors in classification of mosaic status and from this model we estimate that 33% of mosaic mutations in probands contribute to 5.1% of simplex ASD diagnoses (95% credible interval 1.3% to 8.9%). Our results indicate a contributory role for multi-tissue mosaic mutation in some individuals with an ASD diagnosis. Recent sequencing experiments have shown that genetic mutations present in children but not their parents contribute to autism diagnoses in a large fraction of affected families. Here we address the question of whether mutations occurring uniquely in the children arise in the parents’ sperm or egg, or as mosaics in the child after conception. Using a dataset of 2,388 families, we find that while these mutations are primarily inherited from parental germ cells, 5.4% of these mutations appear to arise after conception. Mosaic mutations occur more frequently in probands relative to their unaffected siblings and from this enrichment we estimate that mosaic mutations contribute to 5.1% of autism diagnoses. In addition, we show that brain-specific mutations are not frequently detectable in individuals with an autism diagnosis. Our results indicate that some fraction of identical twins is expected to be discordant for an autism diagnosis due to genetic factors (post-zygotic mutation).

Detection of somatic mutations in tumor samples is important in the clinic, where treatment decisions are increasingly based upon molecular diagnostics. However, accurate detection of these mutations is difficult, due in part to intra-tumor heterogeneity, contamination of the tumor sample with normal tissue and pervasive structural variation. Here, we describe Sentieon TNscope, a haplotype-based somatic variant caller with increased accuracy relative to existing methods. An early engineering version of TNscope was used in our submission to the most recent ICGC-DREAM Somatic Mutation calling challenge. In that challenge, TNscope is the leader in accuracy for SNVs, indels and SVs. To further improve variant calling accuracy, we combined the improvements in the variant caller with machine learning. We benchmarked TNscope using in-silico mixtures of well-characterized Genome in a Bottle (GIAB) samples. TNscope displays higher accuracy than the other benchmarked tools and the accuracy is substantially improved by the machine learning model.