Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing

Coming of age: ten years of next-generation sequencing technologies

Since the completion of the human genome project in 2003, extraordinary progress has been made in genome sequencing technologies, which has led to a decreased cost per megabase and an increase in the number and diversity of sequenced genomes. An astonishing complexity of genome architecture has been revealed, bringing these sequencing technologies to even greater advancements. Some approaches maximize the number of bases sequenced in the least amount of time, generating a wealth of data that can be used to understand increasingly complex phenotypes. Alternatively, other approaches now aim to sequence longer contiguous pieces of DNA, which are essential for resolving structurally complex regions. These and other strategies are providing researchers and clinicians a variety of tools to probe genomes in greater depth, leading to an enhanced understanding of how genome sequence variants underlie phenotype and disease.

Improving Mutation Screening in Patients with Colorectal Cancer Predisposition Using Next-Generation Sequencing

Identification of genetic alterations is important for family risk assessment in colorectal cancers. Next-generation sequencing (NGS) technologies provide useful tools for single-nucleotide and copy number variation (CNV) identification in many genes and samples simultaneously. Herein, we present the validation of current Multiplicom MASTR designs of mismatch repair combined to familial adenomatous polyposis genes in a single PCR reamplification test for eight DNA samples simultaneously on a MiSeq apparatus. Blood samples obtained from 224 patients were analyzed. We correctly identified the 97 mutations selected among 48 samples tested in a validation cohort. PMS2 NGS analysis of the eight positive controls identified single-nucleotide variations not detected with targeted referent methods. As NGS method could not discriminate if some of them were assigned to PMS2 or pseudogenes, only CNV analysis with multiplex ligand probe-dependent amplification confirmation was retained for clinical use. Twenty-seven new variants of unknown significance, 21 disease-causing variants, and two CNVs were detected among the 176 patient samples analyzed in diagnosis routine. MUTYH disease-causing mutations were identified in two patient samples assessed for mismatch repair testing, confirming that this method facilitates accurate and rapid individual risk assessments. In one sample, the MUTYH mutation was associated with a MSH6 disease-causing mutation, suggesting that this method is helpful to identify additional cancer risk modifiers and provides a useful tool to optimize clinical issues.

Next-generation sequencing for endocrine cancers: Recent advances and challenges

Contemporary molecular biology research tools have enriched numerous areas of biomedical research that address challenging diseases, including endocrine cancers (pituitary, thyroid, parathyroid, adrenal, testicular, ovarian, and neuroendocrine cancers). These tools have placed several intriguing clues before the scientific community. Endocrine cancers pose a major challenge in health care and research despite considerable attempts by researchers to understand their etiology. Microarray analyses have provided gene signatures from many cells, tissues, and organs that can differentiate healthy states from diseased ones, and even show patterns that correlate with stages of a disease. Microarray data can also elucidate the responses of endocrine tumors to therapeutic treatments. The rapid progress in next-generation sequencing methods has overcome many of the initial challenges of these technologies, and their advantages over microarray techniques have enabled them to emerge as valuable aids for clinical research applications (prognosis, identification of drug targets, etc.). A comprehensive review describing the recent advances in next-generation sequencing methods and their application in the evaluation of endocrine and endocrine-related cancers is lacking. The main purpose of this review is to illustrate the concepts that collectively constitute our current view of the possibilities offered by next-generation sequencing technological platforms, challenges to relevant applications, and perspectives on the future of clinical genetic testing of patients with endocrine tumors. We focus on recent discoveries in the use of next-generation sequencing methods for clinical diagnosis of endocrine tumors in patients and conclude with a discussion on persisting challenges and future objectives.

Gene and Variant Annotation for Mendelian Disorders in the Era of Advanced Sequencing Technologies

Comprehensive annotations of genetic and noncoding regions and corresponding accurate variant classification for Mendelian diseases are the next big challenge in the new genomic era of personalized medicine. Progress in the development of faster and more accurate pipelines for genome annotation and variant classification will lead to the discovery of more novel disease associations and candidate therapeutic targets. This ultimately will facilitate better patient recruitment in clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics that aims to increase understanding of overall genomic complexity, complex inheritance patterns of disease, and patient-phenotype-specific genomic associations. We describe the emerging field of translational functional genomics, which integrates other functional "-omics" approaches that support next-generation sequencing genomic data in order to facilitate personalized diagnostics, disease management, biomarker discovery, and medicine. We also discuss the utility of this integrated approach for diagnostic clinics and medical databases and its role in the future of personalized medicine.

Clinical Applications of Next-Generation Sequencing in Cancer Diagnosis

With the advancement and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research fields. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is utilized to novel diagnostic and rare cancer mutations, detection of translocations, inversions, insertions and deletions, detection of copy number variants, detect familial cancer mutation carriers, provide the molecular rationale for appropriate targeted, therapeutic and prognostic. NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) and the sensitivity, speed in a single test at a relatively low cost compared to be other sequencing modalities. Here we described the technology, methods and applications that can be immediately considered and some of the challenges that lie ahead.

CNV discovery for milk composition traits in dairy cattle using whole genome resequencing

Background

Copy number variations (CNVs) are important and widely distributed in the genome. CNV detection opens a new avenue for exploring genes associated with complex traits in humans, animals and plants. Herein, we present a genome-wide assessment of CNVs that are potentially associated with milk composition traits in dairy cattle.

Results

In this study, CNVs were detected based on whole genome re-sequencing data of eight Holstein bulls from four half- and/or full-sib families, with extremely high and low estimated breeding values (EBVs) of milk protein percentage and fat percentage. The range of coverage depth per individual was 8.2–11.9×. Using CNVnator, we identified a total of 14,821 CNVs, including 5025 duplications and 9796 deletions. Among them, 487 differential CNV regions (CNVRs) comprising ~8.23 Mb of the cattle genome were observed between the high and low groups. Annotation of these differential CNVRs were performed based on the cattle genome reference assembly (UMD3.1) and totally 235 functional genes were found within the CNVRs. By Gene Ontology and KEGG pathway analyses, we found that genes were significantly enriched for specific biological functions related to protein and lipid metabolism, insulin/IGF pathway-protein kinase B signaling cascade, prolactin signaling pathway and AMPK signaling pathways. These genes included INS, IGF2, FOXO3, TH, SCD5, GALNT18, GALNT16, ART3, SNCA and WNT7A, implying their potential association with milk protein and fat traits. In addition, 95 CNVRs were overlapped with 75 known QTLs that are associated with milk protein and fat traits of dairy cattle (Cattle QTLdb).

Conclusions

In conclusion, based on NGS of 8 Holstein bulls with extremely high and low EBVs for milk PP and FP, we identified a total of 14,821 CNVs, 487 differential CNVRs between groups, and 10 genes, which were suggested as promising candidate genes for milk protein and fat traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3636-3) contains supplementary material, which is available to authorized users.

In Silico identification and annotation of non-coding RNAs by RNA-seq and De Novo assembly of the transcriptome of Tomato Fruits

The complexity of the tomato (Solanum lycopersicum) transcriptome has not yet been fully elucidated. To gain insights into the diversity and features of coding and non-coding RNA molecules of tomato fruits, we generated strand-specific libraries from berries of two tomato cultivars grown in two open-field conditions with different soil type. Following high-throughput Illumina RNA-sequencing (RNA-seq), more than 90% of the reads (over one billion, derived from twelve dataset) were aligned to the tomato reference genome. We report a comprehensive analysis of the transcriptome, improved with 39,095 transcripts, which reveals previously unannotated novel transcripts, natural antisense transcripts, long non-coding RNAs and alternative splicing variants. In addition, we investigated the sequence variants between the cultivars under investigation to highlight their genetic difference. Our strand-specific analysis allowed us to expand the current tomato transcriptome annotation and it is the first to reveal the complexity of the poly-adenylated RNA world in tomato. Moreover, our work demonstrates the usefulness of strand specific RNA-seq approach for the transcriptome-based genome annotation and provides a resource valuable for further functional studies.

Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification

Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided.

RelocaTE2: a high resolution transposable element insertion site mapping tool for population resequencing

Background

Transposable element (TE) polymorphisms are important components of population genetic variation. The functional impacts of TEs in gene regulation and generating genetic diversity have been observed in multiple species, but the frequency and magnitude of TE variation is under appreciated. Inexpensive and deep sequencing technology has made it affordable to apply population genetic methods to whole genomes with methods that identify single nucleotide and insertion/deletion polymorphisms. However, identifying TE polymorphisms, particularly transposition events or non-reference insertion sites can be challenging due to the repetitive nature of these sequences, which hamper both the sensitivity and specificity of analysis tools.

Methods

We have developed the tool RelocaTE2 for identification of TE insertion sites at high sensitivity and specificity. RelocaTE2 searches for known TE sequences in whole genome sequencing reads from second generation sequencing platforms such as Illumina. These sequence reads are used as seeds to pinpoint chromosome locations where TEs have transposed. RelocaTE2 detects target site duplication (TSD) of TE insertions allowing it to report TE polymorphism loci with single base pair precision.

Results and Discussion

The performance of RelocaTE2 is evaluated using both simulated and real sequence data. RelocaTE2 demonstrate high level of sensitivity and specificity, particularly when the sequence coverage is not shallow. In comparison to other tools tested, RelocaTE2 achieves the best balance between sensitivity and specificity. In particular, RelocaTE2 performs best in prediction of TSDs for TE insertions. Even in highly repetitive regions, such as those tested on rice chromosome 4, RelocaTE2 is able to report up to 95% of simulated TE insertions with less than 0.1% false positive rate using 10-fold genome coverage resequencing data. RelocaTE2 provides a robust solution to identify TE insertion sites and can be incorporated into analysis workflows in support of describing the complete genotype from light coverage genome sequencing.

Selective Y centromere inactivation triggers chromosome shattering in micronuclei and repair by non-homologous end joining

Chromosome missegregation into a micronucleus can cause complex and localized genomic rearrangements known as chromothripsis, but the underlying mechanisms remain unresolved. Here we developed an inducible Y centromere-selective inactivation strategy by exploiting a CENP-A/histone H3 chimaera to directly examine the fate of missegregated chromosomes in otherwise diploid human cells. Using this approach, we identified a temporal cascade of events that are initiated following centromere inactivation involving chromosome missegregation, fragmentation, and re-ligation that span three consecutive cell cycles. Following centromere inactivation, a micronucleus harbouring the Y chromosome is formed in the first cell cycle. Chromosome shattering, producing up to 53 dispersed fragments from a single chromosome, is triggered by premature micronuclear condensation prior to or during mitotic entry of the second cycle. Lastly, canonical non-homologous end joining (NHEJ), but not homology-dependent repair, is shown to facilitate re-ligation of chromosomal fragments in the third cycle. Thus, initial errors in cell division can provoke further genomic instability through fragmentation of micronuclear DNAs coupled to NHEJ-mediated reassembly in the subsequent interphase.

ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database (http://chitars.md.biu.ac.il) is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34 922 chimeric transcripts along with 11 714 cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the ‘Full Collection’. In addition, for every chimera, we have added a predicted chimeric protein–protein interaction (ChiPPI) network, which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34 922 chimeric transcripts from eight organisms, and includes the manual annotation of 200 sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins.

Integrating next-generation sequencing into clinical oncology: strategies, promises and pitfalls

We live in an era of genomic medicine. The past five years brought about many significant achievements in the field of cancer genetics, driven by rapidly evolving technologies and plummeting costs of next-generation sequencing (NGS). The official completion of the Cancer Genome Project in 2014 led many to envision the clinical implementation of cancer genomic data as the next logical step in cancer therapy. Stemming from this vision, the term 'precision oncology' was coined to illustrate the novelty of this individualised approach. The basic assumption of precision oncology is that molecular markers detected by NGS will predict response to targeted therapies independently from tumour histology. However, along with a ubiquitous availability of NGS, the complexity and heterogeneity at the individual patient level had to be acknowledged. Not only does the latter present challenges to clinical decision-making based on sequencing data, it is also an obstacle to the rational design of clinical trials. Novel tissue-agnostic trial designs were quickly developed to overcome these challenges. Results from some of these trials have recently demonstrated the feasibility and efficacy of this approach. On the other hand, there is an increasing amount of whole-exome and whole-genome NGS data which allows us to assess ever smaller differences between individual patients with cancer. In this review, we highlight different tumour sequencing strategies currently used for precision oncology, describe their individual strengths and weaknesses, and emphasise their feasibility in different clinical settings. Further, we evaluate the possibility of NGS implementation in current and future clinical trials, and point to the significance of NGS for translational research.

Molecular heterogeneity in the novel fusion gene APIP-FGFR2: Diversity of genomic breakpoints in gastric cancer with high-level amplifications at 11p13 and 10q26

Several novel fusion transcripts were identified by next-generation sequencing in gastric cancer; however, the breakpoint junctions have yet to be characterized. The present study characterized a plethora of APIP-FGFR2 genomic breakpoints in the SNU-16 gastric cancer cell line, which harbored homogeneously staining regions (hsrs) and double minute chromosomes. Oligonucleotide microarrays revealed high-level amplifications at chromosomes 8q24.1 (0.8 Mb region), 10q26 (1.1 Mb) and 11p13 (1.1 Mb). These amplicons contained MYC and PVT1 at chromosome 8q24.1, BRWD2, FGFR2 and ATE1 at chromosome 10q26, and 24 genes, including APIP, CD44, RAG1 and RAG2, at chromosome 11p13. Based on these findings, reverse transcription-polymerase chain reaction (PCR) was performed using various candidate gene primers to detect possible fusion transcripts, and several products using primer sets for the APIP and FGFR2 genes were detected. Eventually, three in-frame and two out-of-frame fusion transcripts were detected. Notably, PCR analysis of the entire genomic DNA detected three distinct genomic junctions. The breakpoints were within intron 5 of APIP, which contained three distinct breakpoints, and introns 5, 7 and 9 of FGFR2. Fluorescence in situ hybridization showed several fusion signals within hsrs using two short probes (~10-kb segments of a bacterial artificial chromosome clone) containing exons 2–5 of APIP or exons 11–13 of FGFR2. Although, for any given fusion, a multiplicity of transcripts is thought to be created by alternative splicing of one rearranged allele, the results of the present study suggested that genomic fusions of APIP and FGFR2 are generated in hsrs with a diversity of breakpoints that are then faithfully transcribed.

Identifying fusion transcripts using next generation sequencing

Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website.

Unexpected effects of different genetic backgrounds on identification of genomic rearrangements via whole-genome next generation sequencing

Background

Whole genome next generation sequencing (NGS) is increasingly employed to detect genomic rearrangements in cancer genomes, especially in lymphoid malignancies. We recently established a unique mouse model by specifically deleting a key non-homologous end-joining DNA repair gene, Xrcc4, and a cell cycle checkpoint gene, Trp53, in germinal center B cells. This mouse model spontaneously develops mature B cell lymphomas (termed G1XP lymphomas).

Results

Here, we attempt to employ whole genome NGS to identify novel structural rearrangements, in particular inter-chromosomal translocations (CTXs), in these G1XP lymphomas. We sequenced six lymphoma samples, aligned our NGS data with mouse reference genome (in C57BL/6J (B6) background) and identified CTXs using CREST algorithm. Surprisingly, we detected widespread CTXs in both lymphomas and wildtype control samples, majority of which were false positive and attributable to different genetic backgrounds. In addition, we validated our NGS pipeline by sequencing multiple control samples from distinct tissues of different genetic backgrounds of mouse (B6 vs non-B6). Lastly, our studies showed that widespread false positive CTXs can be generated by simply aligning sequences from different genetic backgrounds of mouse.

Conclusions

We conclude that mapping and alignment with reference genome might not be a preferred method for analyzing whole-genome NGS data obtained from a genetic background different from reference genome. Given the complex genetic background of different mouse strains or the heterogeneity of cancer genomes in human patients, in order to minimize such systematic artifacts and uncover novel CTXs, a preferred method might be de novo assembly of personalized normal control genome and cancer cell genome, instead of mapping and aligning NGS data to mouse or human reference genome. Thus, our studies have critical impact on the manner of data analysis for cancer genomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3153-9) contains supplementary material, which is available to authorized users.

Anaplastic lymphoma kinase: Role in cancer and therapy perspective

Anaplastic lymphoma kinase (ALK) is correlated with oncogenesis in different types of cancers, such as anaplastic large cell lymphoma, lung cancer, neuroblastoma, and even breast cancer, by abnormal fusion of ALK or non-fusion ALK activation. ALK is a receptor tyrosine kinase, with a single transmembrane domain, that plays an important role in development. Upon ligand binding to the extracellular domain, the receptor undergoes dimerization and subsequent autophosphorylation of the intracellular kinase domain. In recent years, ALK inhibitors have been developed for cancer treatment. These inhibitors target ALK activity and show effectiveness in ALK-positive non-small cell lung cancer. However, acquired treatment resistance makes the future of this therapy unclear; new strategies are underway to overcome the limitations of current ALK inhibitors.

Targeted Cancer Therapy: Vital Oncogenes and a New Molecular Genetic Paradigm for Cancer Initiation Progression and Treatment

It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.

New challenges for BRCA testing: a view from the diagnostic laboratory

Increased demand for BRCA testing is placing pressures on diagnostic laboratories to raise their mutation screening capacity and handle the challenges associated with classifying BRCA sequence variants for clinical significance, for example interpretation of pathogenic mutations or variants of unknown significance, accurate determination of large genomic rearrangements and detection of somatic mutations in DNA extracted from formalin-fixed, paraffin-embedded tumour samples. Many diagnostic laboratories are adopting next-generation sequencing (NGS) technology to increase their screening capacity and reduce processing time and unit costs. However, migration to NGS introduces complexities arising from choice of components of the BRCA testing workflow, such as NGS platform, enrichment method and bioinformatics analysis process. An efficient, cost-effective accurate mutation detection strategy and a standardised, systematic approach to the reporting of BRCA test results is imperative for diagnostic laboratories. This review covers the challenges of BRCA testing from the perspective of a diagnostics laboratory.

Estimating Copy Number and Allelic Variation at the Immunoglobulin Heavy Chain Locus Using Short Reads

The study of genomic regions that contain gene copies and structural variation is a major challenge in modern genomics. Unlike variation involving single nucleotide changes, data on the variation of copy number is difficult to collect and few tools exist for analyzing the variation between individuals. The immunoglobulin heavy variable (IGHV) locus, which plays an integral role in the adaptive immune response, is an example of a complex genomic region that varies in gene copy number. Lack of standard methods to genotype this region prevents it from being included in association studies and is holding back the growing field of antibody repertoire analysis. Here we develop a method that takes short reads from high-throughput sequencing and outputs a genetic profile of the IGHV locus with the read coverage depth and a putative nucleotide sequence for each operationally defined gene cluster. Our operationally defined gene clusters aim to address a major challenge in studying the IGHV locus: the high sequence similarity between gene segments in different genomic locations. Tests on simulated data demonstrate that our approach can accurately determine the presence or absence of a gene cluster from reads as short as 70 bp. More detailed resolution on the copy number of gene clusters can be obtained from read coverage depth using longer reads (e.g., ≥ 100 bp). Detail at the nucleotide resolution of single copy genes (genes present in one copy per haplotype) can be determined with 250 bp reads. For IGHV genes with more than one copy, accurate nucleotide-resolution reconstruction is currently beyond the means of our approach. When applied to a family of European ancestry, our pipeline outputs genotypes that are consistent with the family pedigree, confirms existing multigene variants and suggests new copy number variants. This study paves the way for analyzing population-level patterns of variation in IGHV gene clusters in larger diverse datasets and for quantitatively handling regions of copy number variation in other structurally varying and complex loci.

Circulating Plasma Tumor DNA

Circulating cell-free DNA (ccfDNA)--first identified in 1947--is "naked" DNA that is free-floating in the blood, and derived from both normal and diseased cells. In the 1970s, scientists observed that patients with cancer had elevated levels of ccfDNA as compared to their healthy, cancer-free counterparts. The maternal fetal medicine community first developed techniques to identify the small fraction of fetal-derived ccfDNA for diagnostic purposes. Similarly, due to the presence of tumor-specific (somatic) variations in all cancers, the fraction of circulating cell-free plasma tumor DNA (ptDNA) in the larger pool of ccfDNA derived from normal cells can serve as extremely specific blood-based biomarkers for a patient's cancer. In theory this "liquid biopsy" can provide a real-time assessment of molecular tumor genotype (qualitative) and existing tumor burden (quantitative). Historically, the major limitation for ptDNA as a biomarker has been related to a low detection rate; however, current and developing techniques have improved sensitivity dramatically. In this chapter, we discuss these methods, including digital polymerase chain reaction and various approaches to tagged next-generation sequencing.

Enhanced copy number variants detection from whole-exome sequencing data using EXCAVATOR2

Copy Number Variants (CNVs) are structural rearrangements contributing to phenotypic variation that have been proved to be associated with many disease states. Over the last years, the identification of CNVs from whole-exome sequencing (WES) data has become a common practice for research and clinical purpose and, consequently, the demand for more and more efficient and accurate methods has increased. In this paper, we demonstrate that more than 30% of WES data map outside the targeted regions and that these reads, usually discarded, can be exploited to enhance the identification of CNVs from WES experiments. Here, we present EXCAVATOR2, the first read count based tool that exploits all the reads produced by WES experiments to detect CNVs with a genome-wide resolution. To evaluate the performance of our novel tool we use it for analysing two WES data sets, a population data set sequenced by the 1000 Genomes Project and a tumor data set made of bladder cancer samples. The results obtained from these analyses demonstrate that EXCAVATOR2 outperforms other four state-of-the-art methods and that our combined approach enlarge the spectrum of detectable CNVs from WES data with an unprecedented resolution. EXCAVATOR2 is freely available at http://sourceforge.net/projects/excavator2tool/.

Individualized strategies to target specific mechanisms of disease in malignant melanoma patients displaying unique mutational signatures

Targeted treatment of advanced melanoma could benefit from the precise molecular characterization of melanoma samples. Using a melanoma-specific selection of 217 genes, we performed targeted deep sequencing of a series of biopsies, from advanced melanoma cases, with a Breslow index of ≥4 mm, and/or with a loco-regional infiltration in lymph nodes or presenting distant metastasis, as well of a collection of human cell lines. This approach detected 3–4 mutations per case, constituting unique mutational signatures associated with specific inhibitor sensitivity. Functionally, case-specific combinations of inhibitors that simultaneously targeted MAPK-dependent and MAPK-independent mechanisms were most effective at inhibiting melanoma growth, against each specific mutational background. These observations were challenged by characterizing a freshly resected biopsy from a metastatic lesion located in the skin and soft tissue and by testing its associated therapy ex vivo and in vivo using melanocytes and patient-derived xenografted mice, respectively.

The results show that upon mutational characterization of advanced melanoma patients, specific mutational profiles can be used for selecting drugs that simultaneously target several deregulated genes/pathways involved in tumor generation or progression.

Identification of Genomic Insertion and Flanking Sequence of G2-EPSPS and GAT Transgenes in Soybean Using Whole Genome Sequencing Method

Molecular characterization of sequence flanking exogenous fragment insertion is essential for safety assessment and labeling of genetically modified organism (GMO). In this study, the T-DNA insertion sites and flanking sequences were identified in two newly developed transgenic glyphosate-tolerant soybeans GE-J16 and ZH10-6 based on whole genome sequencing (WGS) method. More than 22.4 Gb sequence data (∼21 × coverage) for each line was generated on Illumina HiSeq 2500 platform. The junction reads mapped to boundaries of T-DNA and flanking sequences in these two events were identified by comparing all sequencing reads with soybean reference genome and sequence of transgenic vector. The putative insertion loci and flanking sequences were further confirmed by PCR amplification, Sanger sequencing, and co-segregation analysis. All these analyses supported that exogenous T-DNA fragments were integrated in positions of Chr19: 50543767-50543792 and Chr17: 7980527-7980541 in these two transgenic lines. Identification of genomic insertion sites of G2-EPSPS and GAT transgenes will facilitate the utilization of their glyphosate-tolerant traits in soybean breeding program. These results also demonstrated that WGS was a cost-effective and rapid method for identifying sites of T-DNA insertions and flanking sequences in soybean.

Do platinum salts fit all triple negative breast cancers?

Triple-negative breast cancer (TNBC) is an aggressive disease with limited treatment options and poor prognosis once metastatic. Pre-clinical and clinical data suggest that TNBC could be more sensitive to platinum-based chemotherapy, especially among BRCA1/2-mutated patients. In recent years, several randomised trials have been conducted to evaluate platinum efficacy in both early-stage and advanced TNBC, with conflicting results especially for long-term outcomes. Experimental studies are now focusing on identifying biomarkers of response to help selecting patients who may benefit most from platinum-based therapies, including BRCA1/2 mutational status and genomic instability signatures (such as HRD-LOH or HRD-LST scores). A standard therapy for TNBC is still missing and platinum-based regimens represent an emerging therapeutic option for selected patients with a defect in the homologous recombination repair system. The identification of these patients through validated biomarker assays will be crucial to optimize the use of currently approved agents in TNBC.

Quantitative detection of low-abundance somatic structural variants in normal cells by high-throughput sequencing

The detection and quantification of low-abundance somatic DNA mutations by high throughput sequencing is challenging because of the difficulty in distinguishing errors from true mutations. While there are several approaches available for analyzing somatic point mutations and small indels, an accurate genome-wide assessment of somatic structural variants (somSVs) in bulk DNA is still not possible. Here we present Structural Variant Search (SVS), a method to accurately detect rare somSVs by low-coverage sequencing. We demonstrate direct quantitative assessment of elevated somSV frequencies induced by known clastogenic compounds in human primary cells.

Alterations to chromatin in intestinal macrophages link IL-10 deficiency to inappropriate inflammatory responses

Intestinal macrophages (IMs) are uniquely programmed to tolerate exposure to bacteria without mounting potent inflammatory responses. The cytokine IL-10 maintains the macrophage anti-inflammatory response such that loss of IL-10 results in chronic intestinal inflammation. To investigate how IL-10-deficiency alters IM programming and bacterial tolerance, we studied changes in chromatin accessibility in response to bacteria in macrophages from two distinct niches, the intestine and bone-marrow, from both wild-type and IL-10-deficient (Il10(-/-) ) mice. We identified chromatin accessibility changes associated with bacterial exposure and IL-10 deficiency in both bone marrow derived macrophages and IMs. Surprisingly, Il10(-/-) IMs adopted chromatin and gene expression patterns characteristic of an inflammatory response, even in the absence of bacteria. Further, when recombinant IL-10 was added to Il10(-/-) cells, it could not revert the chromatin landscape to a normal state. Our results demonstrate that IL-10 deficiency results in stable chromatin alterations in macrophages, even in the absence of bacteria. This supports a model in which IL-10-deficiency leads to chromatin alterations that contribute to a loss of IM tolerance to bacteria, which is a primary initiating event in chronic intestinal inflammation.

Advances in the Molecular Analysis of Soft Tissue Tumors and Clinical Implications

The emergence of high-throughput molecular technologies has accelerated the discovery of novel diagnostic, prognostic and predictive molecular markers. Clinical implementation of these technologies is expected to transform the practice of surgical pathology. In soft tissue tumor pathology, accurate interpretation of comprehensive genomic data provides useful diagnostic and prognostic information, and informs therapeutic decisions. This article reviews recently developed molecular technologies, focusing on their application to the study of soft tissue tumors. Emphasis is made on practical issues relevant to the surgical pathologist. The concept of genomically-informed therapies is presented as an essential motivation to identify targetable molecular alterations in sarcoma.

Chromothripsis and Kataegis Induced by Telomere Crisis

Telomere crisis occurs during tumorigenesis when depletion of the telomere reserve leads to frequent telomere fusions. The resulting dicentric chromosomes have been proposed to drive genome instability. Here, we examine the fate of dicentric human chromosomes in telomere crisis. We observed that dicentric chromosomes invariably persisted through mitosis and developed into 50-200 μm chromatin bridges connecting the daughter cells. Before their resolution at 3-20 hr after anaphase, the chromatin bridges induced nuclear envelope rupture in interphase, accumulated the cytoplasmic 3' nuclease TREX1, and developed RPA-coated single stranded (ss) DNA. CRISPR knockouts showed that TREX1 contributed to the generation of the ssDNA and the resolution of the chromatin bridges. Post-crisis clones showed chromothripsis and kataegis, presumably resulting from DNA repair and APOBEC editing of the fragmented chromatin bridge DNA. We propose that chromothripsis in human cancer may arise through TREX1-mediated fragmentation of dicentric chromosomes formed in telomere crisis.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.

An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology

Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.

Genome-Wide Search for Host Association Factors during Ovine Progressive Pneumonia Virus Infection

Ovine progressive pneumonia virus (OPPV) is an important virus that causes serious diseases in sheep and goats with a prevalence of 36% in the USA. Although OPPV was discovered more than half of a century ago, little is known about the infection and pathogenesis of this virus. In this report, we used RNA-seq technology to conduct a genome-wide probe for cellular factors that are associated with OPPV infection. A total of approximately 22,000 goat host genes were detected of which 657 were found to have been significantly up-regulated and 889 down-regulated at 12 hours post-infection. In addition to previously known restriction factors from other viral infections, a number of factors which may be specific for OPPV infection were uncovered. The data from this RNA-seq study will be helpful in our understanding of OPPV infection, and also for further study in the prevention and intervention of this viral disease.

Leukaemia 'firsts' in cancer research and treatment

Our understanding of cancer biology has been radically transformed over recent years with a more realistic grasp of its multilayered cellular and genetic complexity. These advances are being translated into more selective and effective treatment of cancers and, although there are still considerable challenges, particularly with drug resistance and metastatic disease, many patients with otherwise lethal malignancies now enjoy protracted remissions or cure. One largely unheralded theme of this story is the extent to which new biological insights and novel clinical applications have their origins with leukaemia and related blood cell cancers, including lymphoma. In this Timeline article, I review the remarkable and ground-breaking role that studies in leukaemia have had at the forefront of this progress.

Mechanisms and Consequences of Cancer Genome Instability: Lessons from Genome Sequencing Studies

During tumor evolution, cancer cells can accumulate numerous genetic alterations, ranging from single nucleotide mutations to whole-chromosomal changes. Although a great deal of progress has been made in the past decades in characterizing genomic alterations, recent cancer genome sequencing studies have provided a wealth of information on the detailed molecular profiles of such alterations in various types of cancers. Here, we review our current understanding of the mechanisms and consequences of cancer genome instability, focusing on the findings uncovered through analysis of exome and whole-genome sequencing data. These analyses have shown that most cancers have evidence of genome instability, and the degree of instability is variable within and between cancer types. Importantly, we describe some recent evidence supporting the idea that chromosomal instability could be a major driving force in tumorigenesis and cancer evolution, actively shaping the genomes of cancer cells to maximize their survival advantage.

Whole-Genome Sequencing Reveals Diverse Models of Structural Variations in Esophageal Squamous Cell Carcinoma

Comprehensive identification of somatic structural variations (SVs) and understanding their mutational mechanisms in cancer might contribute to understanding biological differences and help to identify new therapeutic targets. Unfortunately, characterization of complex SVs across the whole genome and the mutational mechanisms underlying esophageal squamous cell carcinoma (ESCC) is largely unclear. To define a comprehensive catalog of somatic SVs, affected target genes, and their underlying mechanisms in ESCC, we re-analyzed whole-genome sequencing (WGS) data from 31 ESCCs using Meerkat algorithm to predict somatic SVs and Patchwork to determine copy-number changes. We found deletions and translocations with NHEJ and alt-EJ signature as the dominant SV types, and 16% of deletions were complex deletions. SVs frequently led to disruption of cancer-associated genes (e.g., CDKN2A and NOTCH1) with different mutational mechanisms. Moreover, chromothripsis, kataegis, and breakage-fusion-bridge (BFB) were identified as contributing to locally mis-arranged chromosomes that occurred in 55% of ESCCs. These genomic catastrophes led to amplification of oncogene through chromothripsis-derived double-minute chromosome formation (e.g., FGFR1 and LETM2) or BFB-affected chromosomes (e.g., CCND1, EGFR, ERBB2, MMPs, and MYC), with approximately 30% of ESCCs harboring BFB-derived CCND1 amplification. Furthermore, analyses of copy-number alterations reveal high frequency of whole-genome duplication (WGD) and recurrent focal amplification of CDCA7 that might act as a potential oncogene in ESCC. Our findings reveal molecular defects such as chromothripsis and BFB in malignant transformation of ESCCs and demonstrate diverse models of SVs-derived target genes in ESCCs. These genome-wide SV profiles and their underlying mechanisms provide preventive, diagnostic, and therapeutic implications for ESCCs.

Novel somatic mutations identified by whole-exome sequencing in muscle-invasive transitional cell carcinoma of the bladder

Transitional cell carcinoma (TCC) is the one of the most commonly observed types of cancer globally. The identification of novel disease-associated genes in TCC has had a significant effect on the diagnosis and treatment of bladder cancer; however, there may be a large number of novel genes that have not been identified. In the present study, the exomes of two individuals who were diagnosed with muscle-invasive TCC (MI-TCC) were sequenced to investigate potential variants. Subsequently, following algorithm and filter analysis, Sanger sequencing was used to validate the results of deep sequencing. Immunohistochemistry (IHC) was employed to observe the differences in HECT, C2 and WW domain-containing E3 ubiquitin protein ligase 1 (HECW1) protein expression between tumor tissues and para-carcinoma tissues. A total of 6 nonsynonymous mutation genes were identified in MI-TCC, identified as copine VII, RNA binding motif protein, X-linked-like 3, acyl-CoA synthetase medium-chain family member 2A, HECW1, zinc finger protein 273 and trichohyalin. Furthermore, 5 cases were identified to possess a HECW1 gene mutation in 61 MI-TCC specimens, and all of these were point mutations located at exon 11 on chromosome 7. The mutation categories of HECW1 had 4 missense mutations and 1 nonsense mutation. IHC revealed that HECW1 protein was expressed at significantly increased levels in MI-TCC compared with normal bladder urothelium (P<0.001). The present study provided a novel approach for investigating genetic changes in the MI-TCC exome, and identified the novel mutant gene HECW1, which may possess a significant role in the pathogenesis of TCC.

An optimal method to segment piecewise poisson distributed signals with application to sequencing data

To analyze the next generation sequencing data, the so-called read depth signal is often segmented with standard segmentation tools. However, these tools usually assume the signal to be a piecewise constant signal and contaminated with zero mean Gaussian noise, and therefore modeling error occurs. This paper models the read depth signal with piecewise Poisson distribution, which is more appropriate to the next generation sequencing mechanism. Based on the proposed model, an opti- mal dynamic programming algorithm with parallel computing is proposed to segment the piecewise signal, and furthermore detect the copy number variation.

DNA Repair and Chromosomal Translocations

The balance between DNA damage, especially double strand breaks, and DNA damage repair is a critical determinant of chromosomal translocation frequency. The non-homologous end-joining repair (NHEJ) pathways seem to play the major role in the generation of chromosomal translocations. The "landscape" of chromosomal translocation identified in malignancies is largely due to selection processes which operate on the growth advantages conveyed to the cells by the functional consequences of chromosomal translocations (i.e., oncogenic fusion proteins and overexpression of oncogenes, both compromising tumor suppressor gene functions). Newer studies have shown that there is an abundance of local rearrangements in many tumors, like small deletions and inversions. A better understanding of the interplay between DNA repair mechanisms and the generation of tumorigenic translocations will, among many other things, depend on an improved understanding of DNA repair mechanisms and their interplay with chromatin and the 3D organization of the interphase nucleus.

Modeling the evolution space of breakage fusion bridge cycles with a stochastic folding process

Breakage-fusion-bridge cycles in cancer arise when a broken segment of DNA is duplicated and an end from each copy joined together. This structure then 'unfolds' into a new piece of palindromic DNA. This is one mechanism responsible for the localised amplicons observed in cancer genome data. Here we study the evolution space of breakage-fusion-bridge structures in detail. We firstly consider discrete representations of this space with 2-d trees to demonstrate that there are [Formula: see text] qualitatively distinct evolutions involving [Formula: see text] breakage-fusion-bridge cycles. Secondly we consider the stochastic nature of the process to show these evolutions are not equally likely, and also describe how amplicons become localized. Finally we highlight these methods by inferring the evolution of breakage-fusion-bridge cycles with data from primary tissue cancer samples.

Landscape of gene fusions in epithelial cancers: seq and ye shall find

Enabled by high-throughput sequencing approaches, epithelial cancers across a range of tissue types are seen to harbor gene fusions as integral to their landscape of somatic aberrations. Although many gene fusions are found at high frequency in several rare solid cancers, apart from fusions involving the ETS family of transcription factors which have been seen in approximately 50% of prostate cancers, several other common solid cancers have been shown to harbor recurrent gene fusions at low frequencies. On the other hand, many gene fusions involving oncogenes, such as those encoding ALK, RAF or FGFR kinase families, have been detected across multiple different epithelial carcinomas. Tumor-specific gene fusions can serve as diagnostic biomarkers or help define molecular subtypes of tumors; for example, gene fusions involving oncogenes such as ERG, ETV1, TFE3, NUT, POU5F1, NFIB, PLAG1, and PAX8 are diagnostically useful. Tumors with fusions involving therapeutically targetable genes such as ALK, RET, BRAF, RAF1, FGFR1-4, and NOTCH1-3 have immediate implications for precision medicine across tissue types. Thus, ongoing cancer genomic and transcriptomic analyses for clinical sequencing need to delineate the landscape of gene fusions. Prioritization of potential oncogenic "drivers" from "passenger" fusions, and functional characterization of potentially actionable gene fusions across diverse tissue types, will help translate these findings into clinical applications. Here, we review recent advances in gene fusion discovery and the prospects for medicine.

Overview of recurrent chromosomal losses in retinoblastoma detected by low coverage next generation sequencing

Genes are frequently lost or gained in malignant tumors and the analysis of these changes can be informative about the underlying tumor biology. Retinoblastoma is a pediatric intraocular malignancy, and since deletions in chromosome 13 have been described in this tumor, we performed genome wide sequencing with the Illumina platform to test whether recurrent losses could be detected in low coverage data from DNA pools of Rb cases. An in silico reference profile for each pool was created from the human genome sequence GRCh37p5; a chromosome integrity score and a graphics 40 Kb window analysis approach, allowed us to identify with high resolution previously reported non random recurrent losses in all chromosomes of these tumors. We also found a pattern of gains and losses associated to clear and dark cytogenetic bands respectively. We further analyze a pool of medulloblastoma and found a more stable genomic profile and previously reported losses in this tumor. This approach facilitates identification of recurrent deletions from many patients that may be biological relevant for tumor development.

Circulating Tumor Cells, DNA, and mRNA: Potential for Clinical Utility in Patients With Melanoma

: Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and messenger RNA (mRNA), collectively termed circulating tumor products (CTPs), represent areas of immense interest from scientists' and clinicians' perspectives. In melanoma, CTP analysis may have clinical utility in many areas, from screening and diagnosis to clinical decision-making aids, as surveillance biomarkers or sources of real-time genetic or molecular characterization. In addition, CTP analysis can be useful in the discovery of new biomarkers, patterns of treatment resistance, and mechanisms of metastasis development. Here, we compare and contrast CTCs, ctDNA, and mRNA, review the extent of translational evidence to date, and discuss how future studies involving both scientists and clinicians can help to further develop this tool for the benefit of melanoma patients.

IMPLICATIONS FOR PRACTICE

Scientific advancement has enabled the rapid development of tools to analyze circulating tumor cells, tumor DNA, and messenger RNA, collectively termed circulating tumor products (CTPs). A variety of techniques have emerged to detect and characterize melanoma CTPs; however, only a fraction has been applied to human subjects. This review summarizes the available human data that investigate clinical utility of CTP in cancer screening, melanoma diagnosis, prognosis, prediction, and genetic or molecular characterization. It provides a rationale for how CTPs may be useful for future research and discusses how clinicians can be involved in developing this exciting new technology.

Detection and screening of chromosomal rearrangements in uterine leiomyomas by long-distance inverse PCR

Genome instability is a hallmark of many tumors and recently, next-generation sequencing methods have enabled analyses of tumor genomes at an unprecedented level. Studying rearrangement-prone chromosomal regions (putative "breakpoint hotspots") in detail, however, necessitates molecular assays that can detect de novo DNA fusions arising from these hotspots. Here we demonstrate the utility of a long-distance inverse PCR-based method for the detection and screening of de novo DNA rearrangements in uterine leiomyomas, one of the most common types of human neoplasm. This assay allows in principle any genomic region suspected of instability to be queried for DNA rearrangements originating there. No prior knowledge of the identity of the fusion partner chromosome is needed. We used this method to screen uterine leiomyomas for rearrangements at genomic locations known to be rearrangement-prone in this tumor type: upstream HMGA2 and within RAD51B. We identified a novel DNA rearrangement upstream of HMGA2 that had gone undetected in an earlier whole-genome sequencing study. In more than 30 additional uterine leiomyoma samples, not analyzed by whole-genome sequencing previously, no rearrangements were observed within the 1,107 bp and 1,996 bp assayed in the RAD51B and HMGA2 rearrangement hotspots. Our findings show that long-distance inverse PCR is a robust, sensitive, and cost-effective method for the detection and screening of DNA rearrangements from solid tumors that should be useful for many diagnostic applications.

Differences in driver genes between smoking-related and non-smoking-related lung cancer in the Chinese population

Recently, non-smoking-related lung cancer was classified as an independent disease entity because it is different from tobacco-associated lung cancer. Non-smoking-related lung cancer occurs more often in women than men, and the predominant histological type is adenocarcinoma (ADC) rather than squamous cell carcinoma. Most of the driver gene alterations that have been identified in ADC in never-smokers include epidermal growth factor receptor mutations, KRAS mutations, echinoderm microtubule-associated protein like 4/anaplastic lymphoma kinase fusion, and ROS1 fusion, among others. Meanwhile, significant progress has been made in the treatment of ADC. However, in comparison with ADC, no such available molecular targets exist for smoking-associated lung cancer, for which treatment strategies are limited. Next-generation sequencing has been widely applied to the discovery of more genetic profiles of lung cancers. This review summarizes the differences between smoking-related and non-smoking-related lung cancer as follows: different somatic mutation burdens, C:G→A:T transversions, common and novel driver genes, and treatment strategies. Overall, smoking-related lung cancer is more complicated than non-smoking-related lung cancer. Furthermore, we review the prevalence of driver genes in smoking-associated and non-smoking-associated lung cancers in the Chinese population.

SAAS-CNV: A Joint Segmentation Approach on Aggregated and Allele Specific Signals for the Identification of Somatic Copy Number Alterations with Next-Generation Sequencing Data

Cancer genomes exhibit profound somatic copy number alterations (SCNAs). Studying tumor SCNAs using massively parallel sequencing provides unprecedented resolution and meanwhile gives rise to new challenges in data analysis, complicated by tumor aneuploidy and heterogeneity as well as normal cell contamination. While the majority of read depth based methods utilize total sequencing depth alone for SCNA inference, the allele specific signals are undervalued. We proposed a joint segmentation and inference approach using both signals to meet some of the challenges. Our method consists of four major steps: 1) extracting read depth supporting reference and alternative alleles at each SNP/Indel locus and comparing the total read depth and alternative allele proportion between tumor and matched normal sample; 2) performing joint segmentation on the two signal dimensions; 3) correcting the copy number baseline from which the SCNA state is determined; 4) calling SCNA state for each segment based on both signal dimensions. The method is applicable to whole exome/genome sequencing (WES/WGS) as well as SNP array data in a tumor-control study. We applied the method to a dataset containing no SCNAs to test the specificity, created by pairing sequencing replicates of a single HapMap sample as normal/tumor pairs, as well as a large-scale WGS dataset consisting of 88 liver tumors along with adjacent normal tissues. Compared with representative methods, our method demonstrated improved accuracy, scalability to large cancer studies, capability in handling both sequencing and SNP array data, and the potential to improve the estimation of tumor ploidy and purity.

Advanced Applications of RNA Sequencing and Challenges

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.