Next-generation Sequencing Projects Research Articles

Reproducible and high sample throughput isomiR next-generation sequencing for cancer diagnosis.

e15013 Background: Next-generation sequencing (NGS) can produce up to 6 Tb of data per run with single-nucleotide accuracy, making it ideal for quantifying isomiRs, which encompass both canonical miRNAs and their variants, for clinical applications. However, NGS has poor reproducibility and low sample throughput in quantifying circulating isomiRs due to significant technical variations and the limitations of the multiplex strategy, as evidenced by the fact that no isomiR NGS technique has been successfully used to diagnose cancer. Methods: To address these challenges, a library construction method including a dual unique-dual-index (DUDI) technology was developed. DUDI uses a pair of Inner UDI (IUDI) and outer UDI (OUDI) to label a sample. Twelve independent batches of isomiR NGS were carried out, including three repeated batches. Each batch included 100 gastric cancer and 100 control plasma samples. Batch effect, correlation coefficient (R), and principal component (PCA) analyses were used to evaluate technical reproducibility. Machine learning binary classification was used to assess biological reproducibility, with each pair of batch data serving interchangeably as both training and testing data. Results: In this multicenter study, over 700G of isomiR data were generated from 402 gastric cancer and 498 control samples, with a maximum error rate of 1 in 7 million isomiRs being assigned to wrong samples. The PCA plot indicates high technical reproducibility across the three repeated batches, shown by the extensive intermingling of data points from each batch and the lack of distinct batch-wise clustering. This observation is reinforced by that the R value for each of 239 isomiRs between the repeated batches are close to 1. While the mutual machine learning validations between the repeated batches yielded ~95% accuracy, indicating high biological reproducibility. The accuracies of the validations between the different batches of different samples range from 70% to 82%. The lower accuracy is as expected, given the high genetic heterogeneity of cancer and the small sample size. Furthermore, the IsomiR differentiated expression profiles from the current NGS study closely match those from prior qPCR studies. Conclusions: The DUDI library construction method can produce reproducible high sample throughput NGS data, yet it is cost-effective and straightforward. The maximum number of samples that can be multiplexed in an NGS project is almost one million, i.e., 976 * 976, as IUDI and OUDI can be any of the 976 designed DUDIs. This number far exceeds the high sample throughput requirements of any NGS application. While the capability to distinguish true biological variations of IsomiRs from technical noise, demonstrated by the high technical and biological reproducibility and concordance with the qPCR data, enables the development of robust machine learning algorithms for cancer diagnostics.

Looking for Plant microRNAs in Human Blood Samples: Bioinformatics Evidence and Perspectives

Literature has proposed the existence of a cross kingdom regulation (CRK) between human and plants. In this context, microRNAs present in edible plants would be acquired through diet by the consumer’s organism and transported via bloodstream to tissues, where they would modulate gene expression. However, the validity of this phenomenon is strongly debated; indeed, some scholars have discussed both the methodologies and the results obtained in previous works. To date, only one study has performed a bioinformatics analysis on small RNA-sequencing data for checking the presence of plant miRNAs (pmiRNAs) in human plasma. For that investigation, the lack of reliable controls, which led to the misidentification of human RNAs as pmiRNAs, has been deeply criticized. Thus, in the present contribution, we aim to demonstrate the existence of pmiRNAs in human blood, adopting a bioinformatics approach characterized by more stringent conditions and filtering. The information obtained from 380 experiments produced in 5 different next generation sequencing (NGS) projects was examined, revealing the presence of 350 circulating pmiRNAs across the analysed data set. Although one of the NGS projects shows results likely to be attributed to sample contamination, the others appear to provide reliable support for the acquisition of pmiRNAs through diet. To infer the potential biological activity of the identified pmiRNAs, we predicted their putative human mRNA targets, finding with great surprise that they appear to be mainly involved in neurogenesis and nervous system development. Unfortunately, no consensus was identified within the sequences of detected pmiRNAs, in order to justify their stability or capability to be preserved in human plasma. We believe that the issue regarding CKR still needs further clarifications, even if the present findings would offer a solid support that this hypothesis is not impossible.

Next-Generation Sequencing (NGS) Identified Species-Specific SSR and SNP Markers, Allow the Unequivocal Identification of Strawberry Tree (Arbutus unedo L.) Germplasm Accessions and Contribute to Assess Their Genetic Relationships.

The strawberry tree (Arbutus unedo L.), an evergreen bush to small tree of the Ericaceae family, is a main component of the natural flora of the Mediterranean basin that also grows profusely through the Iberian Peninsula, southwestern France, and Ireland. The small edible red fruits are usually used to produce preserves, jams, and liquors, as the Portuguese "aguardente de medronho". The leaves and fruits have been used for a long time in traditional medicine, and their bioactive compounds are presently the subject of intense research. A strawberry tree germplasm collection was recently established by the company Corte Velada (Odiáxere, Portugal). A set of 50 germplasm accessions was selected for a breeding program. A next-generation sequencing project was performed, resulting in the establishment of the first strawberry tree genome assembly and further identification of 500 SSR and 500 SNP loci. Individual molecular fingerprints for the unequivocal identification of the selected 50 accessions were established based on 71 markers alleles amplified by 4 SSR and 9 SNP markers. The same species-specific markers alleles combined with 61 random amplified markers amplified by 5 RAPD and 5 ISSR primers were used to assess the genetic variability and genetic relationships among the selected accessions.

Rapid testing of candidate oncogenes and tumour suppressor genes in signal transduction and neoplastic transformation

The COSMIC database (version 94) lists 576 genes in the Cancer Gene Census which have a defined function as drivers of malignancy (oncogenes) or as tumour suppressors (Tier 1). In addition, there are 147 genes with similar functions, but which are less well characterised (Tier 2). Furthermore, next-generation sequencing projects in the context of precision oncology activities are constantly discovering new ones. Since cancer genes differ from their wild-type precursors in numerous molecular and biochemical properties and exert significant differential effects on downstream processes, simple assays that can uncover oncogenic or anti-oncogenic functionality are desirable and may precede more sophisticated analyses. We describe simple functional assays for PTPN11 (protein-tyrosine phosphatase, non-receptor-type 11)/SHP2 mutants, which are typically found in RASopathies and exhibit potential oncogenic activity. We have also designed a functional test for lysyl oxidase (LOX), a prototypical class II tumour suppressor gene whose loss of function may contribute to neoplastic transformation by RAS oncogenes. Moreover, we applied this test to analyse three co-regulated, RAS-responsive genes for transformation-suppressive activity. The integration of these tests into systems biology studies will contribute to a better understanding of cellular networks in cancer.

Loss-of-function variants in PPP1R12A: From isolated sex reversal to holoprosencephaly spectrum and urogenital malformations

In two independent ongoing next-generation sequencing projects for individuals with holoprosencephaly and individuals with disorders of sex development, and through international research collaboration, we identified twelve individuals with de novo loss-of-function (LoF) variants in protein phosphatase 1, regulatory subunit 12a (PPP1R12A), an important developmental gene involved in cell migration, adhesion, and morphogenesis. This gene has not been previously reported in association with human disease, and it has intolerance to LoF as illustrated by a very low observed-to-expected ratio of LoF variants in gnomAD. Of the twelve individuals, midline brain malformations were found in five, urogenital anomalies in nine, and a combination of both phenotypes in two. Other congenital anomalies identified included omphalocele, jejunal, and ileal atresia with aberrant mesenteric blood supply, and syndactyly. Six individuals had stop gain variants, five had a deletion or duplication resulting in a frameshift, and one had a canonical splice acceptor site loss. Murine and human in situ hybridization and immunostaining revealed PPP1R12A expression in the prosencephalic neural folds and protein localization in the lower urinary tract at critical periods for forebrain division and urogenital development. Based on these clinical and molecular findings, we propose the association of PPP1R12A pathogenic variants with a congenital malformations syndrome affecting the embryogenesis of the brain and genitourinary systems and including disorders of sex development.

Measuring Genome Sizes Using Read-Depth, k-mers, and Flow Cytometry: Methodological Comparisons in Beetles (Coleoptera).

Measuring genome size across different species can yield important insights into evolution of the genome and allow for more informed decisions when designing next-generation genomic sequencing projects. New techniques for estimating genome size using shallow genomic sequence data have emerged which have the potential to augment our knowledge of genome sizes, yet these methods have only been used in a limited number of empirical studies. In this project, we compare estimation methods using next-generation sequencing (k-mer methods and average read depth of single-copy genes) to measurements from flow cytometry, a standard method for genome size measures, using ground beetles (Carabidae) and other members of the beetle suborder Adephaga as our test system. We also present a new protocol for using read-depth of single-copy genes to estimate genome size. Additionally, we report flow cytometry measurements for five previously unmeasured carabid species, as well as 21 new draft genomes and six new draft transcriptomes across eight species of adephagan beetles. No single sequence-based method performed well on all species, and all tended to underestimate the genome sizes, although only slightly in most samples. For one species, Bembidion sp. nr. transversale, most sequence-based methods yielded estimates half the size suggested by flow cytometry.

Centralization Within Sub-Experiments Enhances the Biological Relevance of Gene Co-expression Networks: A Plant Mitochondrial Case Study.

Gene co-expression networks (GCNs) can be prepared using a variety of mathematical approaches based on data sampled across diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks are used to identify genes with similar expression dynamics but are prone to introducing false-positive and false-negative relationships, especially in the instance of large and heterogenous datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralization within sub-experiments (CSE). Using a gene set encoding the plant mitochondrial proteome as a case study, our results show that all CSE-based GCNs assessed had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its complexes, than GCNs not using CSE; thus demonstrating that CSE-based GCNs are efficient at predicting canonical functions and associated pathways, here referred to as the core gene network. Furthermore, we show that correlation analyses using CSE-processed data can be used to fine-tune prediction of the function of uncharacterized genes; while its use in combination with analyses based on non-CSE data can augment conventional stress analyses with the innate connections underpinning the dynamic system being examined. Therefore, CSE is an effective alternative method to conventional batch correction approaches, particularly when dealing with large and heterogenous datasets. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide enhanced biological relevance to conventional GCNs by allowing users to delineate a core gene network.Author SummaryGene co-expression networks (GCNs) are the product of a variety of mathematical approaches that identify causal relationships in gene expression dynamics but are prone to the misdiagnoses of false-positives and false-negatives, especially in the instance of large and heterogenous datasets. In light of the burgeoning output of next-generation sequencing projects performed on a variety of species, and developmental or clinical conditions; the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to generate a “core” GCN with enhanced biological relevance. Our method involves a data-centering step that effectively removes all primary treatment/tissue effects, which is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gain in biological relevance resulting from the adoption of this approach was assessed using a plant mitochondrial case study.

Of Retinoids and Organotins: The Evolution of the Retinoid X Receptor in Metazoa.

Nuclear receptors (NRs) are transcription factors accomplishing a multiplicity of functions, essential for organismal homeostasis. Among their numerous members, the retinoid X receptor (RXR) is a central player of the endocrine system, with a singular ability to operate as a homodimer or a heterodimer with other NRs. Additionally, RXR has been found to be a critical actor in various processes of endocrine disruption resulting from the exposure to a known class of xenobiotics termed organotins (e.g., tributyltin (TBT)), including imposex in gastropod molluscs and lipid perturbation across different metazoan lineages. Thus, given its prominent physiological and endocrine role, RXR is present in the genomes of most extant metazoan species examined to date. Here, we expand on the phylogenetic distribution of RXR across the metazoan tree of life by exploring multiple next-generation sequencing projects of protostome lineages. By addressing amino acid residue conservation in combination with cell-based functional assays, we show that RXR induction by 9-cis retinoic acid (9cisRA) and TBT is conserved in more phyla than previously described. Yet, our results highlight distinct activation efficacies and alternative modes of RXR exploitation by the organotin TBT, emphasizing the need for broader species sampling to clarify the mechanistic activation of RXR.

Loss-of-Function Variants in PPP1R12A: From Isolated Sex Reversal to Holoprosencephaly Spectrum and Urogenital Malformations

In two independent ongoing next-generation sequencing projects for individuals with holoprosencephaly and individuals with disorders of sex development, and through international research collaboration, we identified twelve individuals with de novo loss-of-function (LoF) variants in protein phosphatase 1, regulatory subunit 12a (PPP1R12A), an important developmental gene involved in cell migration, adhesion, and morphogenesis. This gene has not been previously reported in association with human disease, and it has intolerance to LoF as illustrated by a very low observed-to-expected ratio of LoF variants in gnomAD. Of the twelve individuals, midline brain malformations were found in five, urogenital anomalies in nine, and a combination of both phenotypes in two. Other congenital anomalies identified included omphalocele, jejunal, and ileal atresia with aberrant mesenteric blood supply, and syndactyly. Six individuals had stop gain variants, five had a deletion or duplication resulting in a frameshift, and one had a canonical splice acceptor site loss. Murine and human in situ hybridization and immunostaining revealed PPP1R12A expression in the prosencephalic neural folds and protein localization in the lower urinary tract at critical periods for forebrain division and urogenital development. Based on these clinical and molecular findings, we propose the association of PPP1R12A pathogenic variants with a congenital malformations syndrome affecting the embryogenesis of the brain and genitourinary systems and including disorders of sex development.

Leishmania Mitochondrial Genomes: Maxicircle Structure and Heterogeneity of Minicircles.

The mitochondrial DNA (mtDNA), which is present in almost all eukaryotic organisms, is a useful marker for phylogenetic studies due to its relative high conservation and its inheritance manner. In Leishmania and other trypanosomatids, the mtDNA (also referred to as kinetoplast DNA or kDNA) is composed of thousands of minicircles and a few maxicircles, catenated together into a complex network. Maxicircles are functionally similar to other eukaryotic mtDNAs, whereas minicircles are involved in RNA editing of some maxicircle-encoded transcripts. Next-generation sequencing (NGS) is increasingly used for assembling nuclear genomes and, currently, a large number of genomic sequences are available. However, most of the time, the mitochondrial genome is ignored in the genome assembly processes. The aim of this study was to develop a pipeline to assemble Leishmania minicircles and maxicircle DNA molecules, exploiting the raw data generated in the NGS projects. As a result, the maxicircle molecules and the plethora of minicircle classes for Leishmania major, Leishmania infantum and Leishmania braziliensis have been characterized. We have observed that whereas the heterogeneity of minicircle sequences existing in a single cell hampers their use for Leishmania typing and classification, maxicircles emerge as an extremely robust genetic marker for taxonomic studies within the clade of kinetoplastids.

A Review of the Application of Next Generation Sequencing (NGS) in Wild Terrestrial Vertebrate Research

In era of sequencing revolution, scientists seek for knowledge about the ever-expanding field of technology, Next Generation Sequence (NGS) to be applied in their research due to its high reliability and rate of discovery. What is NGS? To obtain a detailed understanding about NGS, it is required to look back the history of sequencing and how the NGS stepped into life science. This review paper gives an overview of NGS projects in wild terrestrial vertebrate including applications such as whole genome sequencing and metagenomics.

Exploring the unmapped DNA and RNA reads in a songbird genome

BackgroundA widely used approach in next-generation sequencing projects is the alignment of reads to a reference genome. Despite methodological and hardware improvements which have enhanced the efficiency and accuracy of alignments, a significant percentage of reads frequently remain unmapped. Usually, unmapped reads are discarded from the analysis process, but significant biological information and insights can be uncovered from these data. We explored the unmapped DNA (normal and bisulfite treated) and RNA sequence reads of the great tit (Parus major) reference genome individual. From the unmapped reads we generated de novo assemblies, after which the generated sequence contigs were aligned to the NCBI non-redundant nucleotide database using BLAST, identifying the closest known matching sequence.ResultsMany of the aligned contigs showed sequence similarity to different bird species and genes that were absent in the great tit reference assembly. Furthermore, there were also contigs that represented known P. major pathogenic species. Most interesting were several species of blood parasites such as Plasmodium and Trypanosoma.ConclusionsOur analyses revealed that meaningful biological information can be found when further exploring unmapped reads. For instance, it is possible to discover sequences that are either absent or misassembled in the reference genome, and sequences that indicate infection or sample contamination. In this study we also propose strategies to aid the capture and interpretation of this information from unmapped reads.

MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data.

Predicted deleteriousness of coding variants is a frequently used criterion to filter out variants detected in next-generation sequencing projects and to select candidates impacting on the risk of human diseases. Most available dedicated tools implement a base-to-base annotation approach that could be biased in presence of several variants in the same genetic codon. We here proposed the MACARON program that, from a standard VCF file, identifies, re-annotates and predicts the amino acid change resulting from multiple single nucleotide variants (SNVs) within the same genetic codon. Applied to the whole exome dataset of 573 individuals, MACARON identifies 114 situations where multiple SNVs within a genetic codon induce an amino acid change that is different from those predicted by standard single SNV annotation tool. Such events are not uncommon and deserve to be studied in sequencing projects with inconclusive findings. MACARON is written in python with codes available on the GENMED website (www.genmed.fr). Supplementary data are available at Bioinformatics online.

Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.

BackgroundNext-generation sequencing (NGS) has transformed the life sciences, and many research groups are newly dependent upon computer clusters to store and analyze large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing center at Uppsala University, Sweden, where core hour usage of ∼800 NGS and ∼200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden, and cluster usage of NGS projects with projects from other sciences.ResultsThe number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS projects than for non-NGS projects. We developed usage and efficiency metrics and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat.ConclusionsHosting NGS projects imposes a large administrative burden at UPPMAX due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We provide a set of recommendations for e-infrastructures that host NGS research projects. We provide anonymized versions of our storage, job, and efficiency databases.

Autosomal Recessive NRL Mutations in Patients with Enhanced S-Cone Syndrome.

Enhanced S-cone syndrome (ESCS) is mainly associated with mutations in the NR2E3 gene. However, rare mutations in the NRL gene have been reported in patients with ESCS. We report on an ESCS phenotype in additional patients with autosomal recessive NRL (arNRL) mutations. Three Moroccan patients of two different families with arNRL mutations were enrolled in this study. The mutation in the DNA of one patient, from a consanguineous marriage, was detected by homozygosity mapping. The mutation in the DNA of two siblings from a second family was detected in a targeted next-generation sequencing project. Full ophthalmic examination was performed, including best-corrected visual acuity, slit-lamp biomicroscopy, funduscopy, Goldmann kinetic perimetry, optical coherence tomography, fundus autofluorescence, and extended electroretinography including an amber stimulus on a blue background and a blue stimulus on an amber background. One patient carried a homozygous missense mutation (c.508C>A; p.Arg170Ser) in the NRL gene, whereas the same mutation was identified heterozygously in the two siblings of a second family, in combination with a one base-pair deletion (c.654del; p.Cys219Valfs*4) on the other allele. All patients had reduced visual acuity and showed a typical clumped pigmentary retinal degeneration (CPRD). Foveal schisis-like changes were observed in the oldest patient. An electroretinogram (ERG) under dark-adapted conditions showed absent responses for low stimulus strengths and reduced responses for high stimulus strengths, with constant b-wave latencies despite increasing stimulus strength. A relatively high amplitude was detected with a blue stimulus on an amber background, while an amber stimulus on a blue background showed reduced responses. The arNRL mutations cause a phenotype with typical CPRD. This phenotype has previously been described in patients with ESCS caused by NR2E3 mutations, and rarely by NRL mutations. Based on our findings in ERG testing, we conclude that S-cone function is enhanced in our patients in a similar manner as in patients with NR2E3-associated ESCS, confirming previous reports of NRL as a second gene to cause ESCS.

Don't just dump your data and run: Authors should submit as much experimental information as possible when uploading sequence data.

If you have, in any way, been involved with genetic research over the past 10 years, then you have likely heard of the Sequence Read Archive (SRA), which is jointly housed at the National Center for Biotechnology (NCBI), the DNA Data Bank of Japan (DDBJ), and the European Bioinformatics Institute (EBI). And if you regularly work with genome or transcriptome sequence information, then you have probably extracted data from and/or deposited data into the SRA. For those who are unfamiliar with it, the SRA is an international public online archive for next‐generation sequencing (NGS) data, which was established about a decade ago under the guidance of the International Nucleotide Sequence Database Collaboration (INSDC) [1], [2]. Despite nearly being shut down in 2011 [3], it has grown at a staggering rate over the past 10 years. As of September 1, 2017, it housed over five quadrillion (1015) open‐access bases of NGS data, coming from thousands of different species and spanning the entire gamut of cellular and viral life. It contains DNA‐ and RNA‐sequencing (DNA‐seq and RNA‐seq) reads of every kind, from bisulfite‐seq to strand‐specific RNA‐seq to single‐cell DNA‐seq, and it accepts reads from every type of NGS platform, be it Illumina, Ion Torrent, or PacBio sequencing. In other words, the SRA is a crucial and central resource in the fast‐paced and increasingly important domain of contemporary genetic research. > … the SRA is a crucial and central resource in the fast‐paced and increasingly important domain of contemporary genetic research. ### The Sequence Read Archive The SRA can be easily accessed and searched via the NCBI (https://www.ncbi.nlm.nih.gov/sra), DDBJ (http://trace.ddbj.nig.ac.jp/dra/index_e.html), and EBI (http://www.ebi.ac.uk/ena/submit/read-submission) websites. Once there, you will find yourself at a sequencing‐read superstore. With a decent Wi‐Fi connection, a couple of keyword searches, and a few clicks of the trackpad, you can quickly …

Comparative Genomic and Phylogenetic Analysis of a Shiga Toxin Producing Shigella sonnei (STSS) Strain.

Shigella strains are important agents of bacillary dysentery, and in recent years Shigella sonnei has emerged as the leading cause of shigellosis in industrialized and rapidly developing countries. More recently, several S. sonnei and Shigella flexneri strains producing Shiga toxin (Stx) have been reported from sporadic cases and from an outbreak in America. In the present study we aimed to shed light on the evolution of a recently identified Shiga toxin producing S. sonnei (STSS) isolated in Europe. Here we report the first completely assembled whole genome sequence of a multidrug resistant (MDR) Stx-producing S. sonnei (STSS) clinical strain and reveal its phylogenetic relations. STSS 75/02 proved to be resistant to ampicillin, streptomycin, tetracycline, chloramphenicol, thrimetoprim, and sulfomethoxazol. The genome of STSS 75/02 contains a 4,891,717 nt chromosome and seven plasmids including the 214 kb invasion plasmid (pInv) harboring type III secretion system genes and associated effectors. The chromosome harbors 23 prophage regions including the Stx1 converting prophage. The genome carries all virulence determinants necessary for an enteroinvasive lifestyle, as well as the Stx1 encoding gene cluster within an earlier described inducible converting prophage. In silico SNP genotyping of the assembled genome as well as 438 complete or draft S. sonnei genomes downloaded from NCBI GenBank revealed that S. sonnei 75/02 belongs to the more recently diverged global MDR lineage (IIIc). Targeted screening of 1131 next-generation sequencing projects taken from NCBI Short Read Archive of confirms that only a few S. sonnei isolates are Stx positive. Our results suggest that the acquisition of Stx phages could have occurred in different environments as independent events and that multiple horizontal transfers are responsible for the appearance of Stx phages in S. sonnei strains.

International Standards for Genomes, Transcriptomes, and Metagenomes.

Challenges and biases in preparing, characterizing, and sequencing DNA and RNA can have significant impacts on research in genomics across all kingdoms of life, including experiments in single-cells, RNA profiling, and metagenomics (across multiple genomes). Technical artifacts and contamination can arise at each point of sample manipulation, extraction, sequencing, and analysis. Thus, the measurement and benchmarking of these potential sources of error are of paramount importance as next-generation sequencing (NGS) projects become more global and ubiquitous. Fortunately, a variety of methods, standards, and technologies have recently emerged that improve measurements in genomics and sequencing, from the initial input material to the computational pipelines that process and annotate the data. Here we review current standards and their applications in genomics, including whole genomes, transcriptomes, mixed genomic samples (metagenomes), and the modified bases within each (epigenomes and epitranscriptomes). These standards, tools, and metrics are critical for quantifying the accuracy of NGS methods, which will be essential for robust approaches in clinical genomics and precision medicine.

A Hidden Markov Model Approach for Simultaneously Estimating Local Ancestry and Admixture Time Using Next Generation Sequence Data in Samples of Arbitrary Ploidy.

Admixture—the mixing of genomes from divergent populations—is increasingly appreciated as a central process in evolution. To characterize and quantify patterns of admixture across the genome, a number of methods have been developed for local ancestry inference. However, existing approaches have a number of shortcomings. First, all local ancestry inference methods require some prior assumption about the expected ancestry tract lengths. Second, existing methods generally require genotypes, which is not feasible to obtain for many next-generation sequencing projects. Third, many methods assume samples are diploid, however a wide variety of sequencing applications will fail to meet this assumption. To address these issues, we introduce a novel hidden Markov model for estimating local ancestry that models the read pileup data, rather than genotypes, is generalized to arbitrary ploidy, and can estimate the time since admixture during local ancestry inference. We demonstrate that our method can simultaneously estimate the time since admixture and local ancestry with good accuracy, and that it performs well on samples of high ploidy—i.e. 100 or more chromosomes. As this method is very general, we expect it will be useful for local ancestry inference in a wider variety of populations than what previously has been possible. We then applied our method to pooled sequencing data derived from populations of Drosophila melanogaster on an ancestry cline on the east coast of North America. We find that regions of local recombination rates are negatively correlated with the proportion of African ancestry, suggesting that selection against foreign ancestry is the least efficient in low recombination regions. Finally we show that clinal outlier loci are enriched for genes associated with gene regulatory functions, consistent with a role of regulatory evolution in ecological adaptation of admixed D. melanogaster populations. Our results illustrate the potential of local ancestry inference for elucidating fundamental evolutionary processes.

Oncoscore, a Novel, Internet-Based Tool to Assess the Oncogenic Potential of Genes Can Differentiate Between CP-CML and BC-CML Associated Genes, and Between CP-CML Patients with Good and Bad Prognosis

INTRODUCTIONThe huge amount of data emerging from Next-Generation Sequencing (NGS) projects is bringing a revolution in cancer medicine, leading to the discovery of a large number of new somatic alterations that are associated with the onset and/or progression of cancer. However, researchers are facing a formidable challenge in identifying oncogenic driver genes among the variants generated by NGS experiments.METHODSTo overcome these limitations, we developed OncoScore, a bioinformatics text-mining tool capable of automatically scanning the biomedical literature by means of dynamically updatable web queries and measuring gene-specific cancer association in terms of gene citations (submitted). OncoScore is distributed as a R Bioconductor package (https://bioconductor.org/packages/release/bioc/html/OncoScore.html) and as a web tool (http://www.galseq.com/oncoscore.html).To assess the ability of OncoScore to discriminate between cancer and non-cancer genes we generated the OncoScore estimation for the whole Cancer Genes Census (CGC; 507 genes) dataset and for a manually curated list of genes not associated to cancer (nCan; 302 genes). The distribution of OncoScore values differed significantly between the two groups (mean: 48.8 and 14.8 for CGC and nCan, respectively; p-value = 2.2e-16). The receiver operating characteristic (ROC) curve and the area under the curve (AUC) metrics confirmed the excellent capability of OncoScore in discriminating the true positive from the true negative cancer genes at different cut-off values (OncoScore cut-off threshold = 21.09; AUC1 = 90.3%, 95% CI: 88.1-92.5).RESULTSTo test OncoScore, we generated whole-exome sequencing data on 33 Chronic Myeloid Leukemia patients, 23 in Chronic Phase (CP) and 10 in Blast Crisis (BC). In order to selectively identify somatic mutations, CP samples were filtered against the germline exome data, while BC was filtered against the corresponding CP exome (thus filtering out all passenger mutations present at diagnosis). A total of 107 and 34 nonsynonymous somatic variants were identified in CP and BC, respectively. The mean OncoScore value of the BC samples was higher than that of CP ones (mean OncoScore = 35.6 ± 4.91 SEM vs. 19.2 ± 2.07 SEM; p=0.0007). Manual inspection of the 10 BC genes with the highest OncoScore values highlighted the presence of at least 5 genes (ABL1, NRAS, ASXL1, RUNX1, IKZF1) that were demonstrated to be functionally associated with CML progression, suggesting that OncoScore was able to correctly prioritize biologically relevant cancer genes relevant to CML progression.The 107 non-synonymous variants (range 0-11 per patient) identified in CP-CML patients at diagnosis were further analyzed. A positive correlation was observed between number of mutations and patient age (r=0.4638; p=0.0258), indicating that several events were passenger mutations being expanded by neoplastic transformation. However, when using OncoScore to weigh the oncogenic potential of each mutation, a significant correlation was observed between the Sokal score and OncoScore (r=0.6815, p=0.0003) suggesting that the identified mutations may have clinical impact.. On long term follow-up (>2 years), 17 CML patients remained on standard imatinib therapy while 6 were shifted to 2nd/3rdline therapy because of resistance to imatinib. According to OncoScore, the 6 imatinib-resistant patients had significantly higher scores at diagnosis than the 17 long-term imatinib responders (measured as the sum of all mutations with Oncoscore values >21: 120±16 SEM vs. 50±15; p=0.0098). No significant differences were instead observed in the Sokal score among the two groups (1.15±0.3 vs. 0.9±0.1, p=0.244). Among the identified genes with high OncoScore values, only ASXL1 was associated with hematological malignancies.CONCLUSIONSThese data indicate that OncoScore is a useful prioritization tool for the annotation of somatic mutations in human cancer. Genes mutated selectively in BC samples had higher OncoScore values than genes mutated in CP at diagnosis (which contain also many passenger mutations). In addition, OncoScore can identify some genes mutated in CML patients at diagnosis which could be relevant for response to treatment, although not usually linked to hematological malignancies. DisclosuresGambacorti-Passerini:Pfizer: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy.