Advances in sequencing technologies have dramatically changed the field of ancient DNA (aDNA). It is now possible to generate an enormous quantity of aDNA sequence data both rapidly and ...inexpensively. As aDNA sequences are generally short in length, damaged, and at low copy number relative to coextracted environmental DNA, high-throughput approaches offer a tremendous advantage over traditional sequencing approaches in that they enable a complete characterization of an aDNA extract. However, the particular qualities of aDNA also present specific limitations that require careful consideration in data analysis. For example, results of high-throughout analyses of aDNA libraries may include chimeric sequences, sequencing error and artifacts, damage, and alignment ambiguities due to the short read lengths. Here, I describe typical primary data analysis workflows for high-throughput aDNA sequencing experiments, including (1) separation of individual samples in multiplex experiments; (2) removal of protocol-specific library artifacts; (3) trimming adapter sequences and merging paired-end sequencing data; (4) base quality score filtering or quality score propagation during data analysis; (5) identification of endogenous molecules from an environmental background; (6) quantification of contamination from other DNA sources; and (7) removal of clonal amplification products or the compilation of a consensus from clonal amplification products, and their exploitation for estimation of library complexity.
Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, ...but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies.
It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants.
We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu ), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance.
While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.
Abstract
Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly ...penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.
Recent advances in DNA sequencing have revolutionized the field of genomics, making it possible for even single research groups to generate large amounts of sequence data very rapidly and at a ...substantially lower cost. These high-throughput sequencing technologies make deep transcriptome sequencing and transcript quantification, whole genome sequencing and resequencing available to many more researchers and projects. However, while the cost and time have been greatly reduced, the error profiles and limitations of the new platforms differ significantly from those of previous sequencing technologies. The selection of an appropriate sequencing platform for particular types of experiments is an important consideration, and requires a detailed understanding of the technologies available; including sources of error, error rate, as well as the speed and cost of sequencing. We review the relevant concepts and compare the issues raised by the current high-throughput DNA sequencing technologies. We analyze how future developments may overcome these limitations and what challenges remain.
The large amount of DNA sequence data generated by high-throughput sequencing technologies often allows multiple samples to be sequenced in parallel on a single sequencing run. This is particularly ...true if subsets of the genome are studied rather than complete genomes. In recent years, target capture from sequencing libraries has largely replaced polymerase chain reaction (PCR) as the preferred method of target enrichment. Parallelizing target capture and sequencing for multiple samples requires the incorporation of sample-specific barcodes into sequencing libraries, which is necessary to trace back the sample source of each sequence. This protocol describes a fast and reliable method for the preparation of barcoded ("indexed") sequencing libraries for Illumina's Genome Analyzer platform. The protocol avoids expensive commercial library preparation kits and can be performed in a 96-well plate setup using multi-channel pipettes, requiring not more than two or three days of lab work. Libraries can be prepared from any type of double-stranded DNA, even if present in subnanogram quantity.
Advances in DNA sequencing technologies have made it possible to generate large amounts of sequence data very rapidly and at substantially lower cost than capillary sequencing. These new technologies ...have specific characteristics and limitations that require either consideration during project design, or which must be addressed during data analysis. Specialist skills, both at the laboratory and the computational stages of project design and analysis, are crucial to the generation of high quality data from these new platforms. The Illumina sequencers (including the Genome Analyzers I/II/IIe/IIx and the new HiScan and HiSeq) represent a widely used platform providing parallel readout of several hundred million immobilized sequences using fluorescent-dye reversible-terminator chemistry. Sequencing library quality, sample handling, instrument settings and sequencing chemistry have a strong impact on sequencing run quality. The presence of adapter chimeras and adapter sequences at the end of short-insert molecules, as well as increased error rates and short read lengths complicate many computational analyses. We discuss here some of the factors that influence the frequency and severity of these problems and provide solutions for circumventing these. Further, we present a set of general principles for good analysis practice that enable problems with sequencing runs to be identified and dealt with.
Next-generation sequencing technologies have revolutionized the field of paleogenomics, allowing the reconstruction of complete ancient genomes and their comparison with modern references. However, ...this requires the processing of vast amounts of data and involves a large number of steps that use a variety of computational tools. Here we present PALEOMIX (http://geogenetics.ku.dk/publications/paleomix), a flexible and user-friendly pipeline applicable to both modern and ancient genomes, which largely automates the in silico analyses behind whole-genome resequencing. Starting with next-generation sequencing reads, PALEOMIX carries out adapter removal, mapping against reference genomes, PCR duplicate removal, characterization of and compensation for postmortem damage, SNP calling and maximum-likelihood phylogenomic inference, and it profiles the metagenomic contents of the samples. As such, PALEOMIX allows for a series of potential applications in paleogenomics, comparative genomics and metagenomics. Applying the PALEOMIX pipeline to the three ancient and seven modern Phytophthora infestans genomes as described here takes 5 d using a 16-core server.
While the number and identity of proteins expressed in a single human cell type is currently unknown, this fundamental question can be addressed by advanced mass spectrometry (MS)‐based proteomics. ...Online liquid chromatography coupled to high‐resolution MS and MS/MS yielded 166 420 peptides with unique amino‐acid sequence from HeLa cells. These peptides identified 10 255 different human proteins encoded by 9207 human genes, providing a lower limit on the proteome in this cancer cell line. Deep transcriptome sequencing revealed transcripts for nearly all detected proteins. We calculate copy numbers for the expressed proteins and show that the abundances of >90% of them are within a factor 60 of the median protein expression level. Comparisons of the proteome and the transcriptome, and analysis of protein complex databases and GO categories, suggest that we achieved deep coverage of the functional transcriptome and the proteome of a single cell type.
More than 10 000 proteins were identified by high‐resolution mass spectrometry in a human cancer cell line. The data cover most of the functional proteome as judged by RNA‐seq data and it reveals the expression range of different protein classes.
The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of ...noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we perform saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitutions and deletions. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and comprise a rich dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations.
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