SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data.

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.

Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment.

Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

TeXP: Deconvolving the effects of pervasive and autonomous transcription of transposable elements.

The Long interspersed nuclear element 1 (LINE-1) is a primary source of genetic variation in humans and other mammals. Despite its importance, LINE-1 activity remains difficult to study because of its highly repetitive nature. Here, we developed and validated a method called TeXP to gauge LINE-1 activity accurately. TeXP builds mappability signatures from LINE-1 subfamilies to deconvolve the effect of pervasive transcription from autonomous LINE-1 activity. In particular, it apportions the multiple reads aligned to the many LINE-1 instances in the genome into these two categories. Using our method, we evaluated well-established cell lines, cell-line compartments and healthy tissues and found that the vast majority (91.7%) of transcriptome reads overlapping LINE-1 derive from pervasive transcription. We validated TeXP by independently estimating the levels of LINE-1 autonomous transcription using ddPCR, finding high concordance. Next, we applied our method to comprehensively measure LINE-1 activity across healthy somatic cells, while backing out the effect of pervasive transcription. Unexpectedly, we found that LINE-1 activity is present in many normal somatic cells. This finding contrasts with earlier studies showing that LINE-1 has limited activity in healthy somatic tissues, except for neuroprogenitor cells. Interestingly, we found that the amount of LINE-1 activity was associated with the with the amount of cell turnover, with tissues with low cell turnover rates (e.g. the adult central nervous system) showing lower LINE-1 activity. Altogether, our results show how accounting for pervasive transcription is critical to accurately quantify the activity of highly repetitive regions of the human genome.

TRCMGene: A two-step referential compression method for the efficient storage of genetic data.

BACKGROUND: The massive quantities of genetic data generated by high-throughput sequencing pose challenges to data storage, transmission and analyses. These problems are effectively solved through data compression, in which the size of data storage is reduced and the speed of data transmission is improved. Several options are available for compressing and storing genetic data. However, most of these options either do not provide sufficient compression rates or require a considerable length of time for decompression and loading. RESULTS: Here, we propose TRCMGene, a lossless genetic data compression method that uses a referential compression scheme. The novel concept of two-step compression method, which builds an index structure using K-means and k-nearest neighbours, is introduced to TRCMGene. Evaluation with several real datasets revealed that the compression factor of TRCMGene ranges from 9 to 21. TRCMGene presents a good balance between compression factor and reading time. On average, the reading time of compressed data is 60% of that of uncompressed data. Thus, TRCMGene not only saves disc space but also saves file access time and speeds up data loading. These effects collectively improve genetic data storage and transmission in the current hardware environment and render system upgrades unnecessary. TRCMGene, user manual and demos could be accessed freely from https://github.com/tangyou79/TRCM. The data mentioned in this manuscript could be downloaded from: https://github.com/tangyou79/TRCM/wiki.

Genetic strategies to bring islet xenotransplantation to the clinic.

PURPOSE OF REVIEW: Despite the benefits of islet and pancreas allotransplantation, their widespread use in type 1 diabetes is limited because of the paucity of suitable donors. Porcine xenotransplantation offers an alternative, and advances in genetic modification of pigs have opened up new potential for its clinical use. This review outlines the barriers to successful islet xenotransplantation, and genetic modifications that have been tested to overcome these. RECENT FINDINGS: Islets from pigs lacking α1,3-galactosyltransferase, to prevent hyperacute rejection, are now used as a background strain for further genetic modifications. The instant blood-mediated inflammatory reaction is overcome by expressing complement regulators including CD46, CD55 and CD59. Prevention of immune-mediated rejection mediated by T cells, macrophages and natural killer cells remains a challenge. The use of immunosuppressive antibodies, such as anti-CD154 or anti-CD2, can be protective, and may be useful if they are produced by the islets themselves. SUMMARY: The field of xenotransplantation has benefited enormously from the development of new genetic modification strategies. With the possibility of multiple genetic modifications in the same animal, and a detailed knowledge of the mechanism of xenograft rejection, the challenge now is to develop islets that provide long-term graft survival without systemic immunosuppression.

Power(2): the power of yeast genetics applied to the powerhouse of the cell.

The budding yeast Saccharomyces cerevisiae has served as a remarkable model organism for numerous seminal discoveries in biology. This paradigm extends to the mitochondria, a central hub for cellular metabolism, where studies in yeast have helped to reinvigorate the field and launch an exciting new era in mitochondrial biology. Here we discuss a few recent examples in which yeast research has laid a foundation for our understanding of evolutionarily conserved mitochondrial processes and functions, from key factors and pathways involved in the assembly of oxidative phosphorylation (OXPHOS) complexes to metabolite transport, lipid metabolism, and interorganelle communication. We also highlight new areas of yeast mitochondrial biology that are likely to aid in our understanding of the mitochondrial etiology of disease in the future.

Discriminating the effects of phylogenetic hypothesis, tree resolution and clade age estimates on phylogenetic signal measurements.

Understanding how species traits evolved over time is the central question to comprehend assembly rules that govern the phylogenetic structure of communities. The measurement of phylogenetic signal (PS) in ecologically relevant traits is a first step to understand phylogenetically structured community patterns. The different methods available to estimate PS make it difficult to choose which is most appropriate. Furthermore, alternative phylogenetic tree hypotheses, node resolution and clade age estimates might influence PS measurements. In this study, we evaluated to what extent these parameters affect different methods of PS analysis, and discuss advantages and disadvantages when selecting which method to use. We measured fruit/seed traits and flowering/fruiting phenology of endozoochoric species occurring in Southern Brazilian Araucaria forests and evaluated their PS using Mantel regressions, phylogenetic eigenvector regressions (PVR) and K statistic. Mantel regressions always gave less significant results compared to PVR and K statistic in all combinations of phylogenetic trees constructed. Moreover, a better phylogenetic resolution affected PS, independently of the method used to estimate it. Morphological seed traits tended to show higher PS than diaspores traits, while PS in flowering/fruiting phenology depended mostly on the method used to estimate it. This study demonstrates that different PS estimates are obtained depending on the chosen method and the phylogenetic tree resolution. This finding has implications for inferences on phylogenetic niche conservatism or ecological processes determining phylogenetic community structure.

Biodiversity technologies: tools as change agents.

A meeting on Biodiversity Technologies was held by the Biodiversity Institute, Oxford on the 27-28 of September 2012 at the Department of Zoology, University of Oxford. The symposium brought together 36 speakers from North America, Australia and across Europe, presenting the latest research on emerging technologies in biodiversity science and conservation. Here we present a perspective on the general trends emerging from the symposium.

Network medicine approaches to the genetics of complex diseases.

Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.

nMETR: technique for facile recovery of hypomethylation genomic tags.

Genome-wide methylation studies frequently lack adequate controls to estimate proportions of background reads in the resulting datasets. To generate appropriate control pools, we developed technique termed nMETR (non-methylated tag recovery) based on digestion of genomic DNA with methylation-sensitive restriction enzyme, ligation of adapter oligonucleotide and PCR amplification of non-methylated sites associated with genomic repetitive elements. The protocol takes only two working days to generate amplicons for deep sequencing. We applied nMETR for human DNA using BspFNI enzyme and retrotransposon Alu-specific primers. 454-sequencing enabled identification of 1113 nMETR tag sites, of them ~65% were parts of CpG islands. Representation of reads inversely correlated with methylation levels, thus confirming nMETR fidelity. We created software that eliminates background reads and enables to map and annotate individual tags on human genome. nMETR tags may serve as the controls for large-scale epigenetic studies and for identifying unmethylated transposable elements located close to genomic CpG islands.

Methylation-specific ligation detection reaction (msLDR): a new approach for multiplex evaluation of methylation patterns.

A new sensitive method for multiplex gene-specific methylation analysis was developed using a ligation-based approach combined with a TaqMan-based detection and readout employing universal reporter probes. The approach, termed methylation-specific Ligation Detection Reaction (msLDR), was applied to test 16 loci in 8 different colorectal cancer cells in parallel. These loci encode immune regulatory genes involved in T-cell and natural killer cell activation, whose silencing is associated with the development or progression of colorectal cancer. Parallel analysis of HLA-A, HLA-B, STAT1, B2M, LMP2, LMP7, PA28α, TAP1, TAP2, TAPBP, ULBP2 and ULBP3 by msLDR in eight colorectal cancer cell lines showed preferential methylation at the HLA-B, ULBP2 and ULBB3 loci, but not at the other loci. MsLDR was found to represent a suitable and sensitive method for the detection of distinct methylation patterns as validated by conventional bisulphite Sanger sequencing and COBRA analysis. Since gene silencing by epigenetic mechanisms plays a central role during transformation of a normal differentiated somatic cell into a cancer cell, characterization of the gene methylation status in tumours is a major topic not only in basic research, but also in clinical diagnostics. Due to a very simple workflow, msLDR is likely to be applicable to clinical samples and thus comprises a potential diagnostic tool for clinical purposes.

Analysis of gene-gene interactions.

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Patently unpatentable: implications of the Myriad court decision on genetic diagnostics.

The recent decision in the case Association for Molecular Pathology et al. v. United States Patent and Trademark Office et al. shocked the biotechnology industry. Although the case could be overturned on appeal, it will probably change how gene patents are written. The effects of the decision might be most strongly felt in the short term by clinical laboratories that develop new genetic tests based on single genes. However, evidence suggests that patents are less effective as an incentive to innovate in the field of genetic diagnostics than for pharmaceuticals. In addition, as genomic technologies move towards whole-genome analysis, policy arguments for patent protection for single genes become less compelling. It is clear that the intellectual property model challenged by the Myriad decision will have to be replaced if new genetic technologies are to achieve their full potential in promoting 'the progress of science and useful arts'.

Effectiveness of strategies to increase the validity of findings from association studies: size vs. replication.

BACKGROUND: The capacity of multiple comparisons to produce false positive findings in genetic association studies is abundantly clear. To address this issue, the concept of false positive report probability (FPRP) measures "the probability of no true association between a genetic variant and disease given a statistically significant finding". This concept involves the notion of prior probability of an association between a genetic variant and a disease, making it difficult to achieve acceptable levels for the FPRP when the prior probability is low. Increasing the sample size is of limited efficiency to improve the situation. METHODS: To further clarify this problem, the concept of true report probability (TRP) is introduced by analogy to the positive predictive value (PPV) of diagnostic testing. The approach is extended to consider the effects of replication studies. The formula for the TRP after k replication studies is mathematically derived and shown to be only dependent on prior probability, alpha, power, and number of replication studies. RESULTS: Case-control association studies are used to illustrate the TRP concept for replication strategies. Based on power considerations, a relationship is derived between TRP after k replication studies and sample size of each individual study. That relationship enables study designers optimization of study plans. Further, it is demonstrated that replication is efficient in increasing the TRP even in the case of low prior probability of an association and without requiring very large sample sizes for each individual study. CONCLUSIONS: True report probability is a comprehensive and straightforward concept for assessing the validity of positive statistical testing results in association studies. By its extension to replication strategies it can be demonstrated in a transparent manner that replication is highly effective in distinguishing spurious from true associations. Based on the generalized TRP method for replication designs, optimal research strategy and sample size planning become possible.

Principles and challenges of genomewide DNA methylation analysis.

Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understanding the influence of epigenetics. There has been a revolution in DNA methylation analysis technology over the past decade: analyses that previously were restricted to specific loci can now be performed on a genome-scale and entire methylomes can be characterized at single-base-pair resolution. However, there is such a diversity of DNA methylation profiling techniques that it can be challenging to select one. This Review discusses the different approaches and their relative merits and introduces considerations for data analysis.

Optimization methods for selecting founder individuals for captive breeding or reintroduction of endangered species.

Methods from genetics and genomics can be employed to help save endangered species. One potential use is to provide a rational strategy for selecting a population of founders for a captive breeding program. The hope is to capture most of the available genetic diversity that remains in the wild population, to provide a safe haven where representatives of the species can be bred, and eventually to release the progeny back into the wild. However, the founders are often selected based on a random-sampling strategy whose validity is based on unrealistic assumptions. Here we outline an approach that starts by using cutting-edge genome sequencing and genotyping technologies to objectively assess the available genetic diversity. We show how combinatorial optimization methods can be applied to these data to guide the selection of the founder population. In particular, we develop a mixed-integer linear programming technique that identifies a set of animals whose genetic profile is as close as possible to specified abundances of alleles (i.e., genetic variants), subject to constraints on the number of founders and their genders and ages.

Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.

This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.

Statistical methods for evolutionary trees.

In 1963 and 1964, L. L. Cavalli-Sforza and A. W. F. Edwards introduced novel methods for computing evolutionary trees from genetical data, initially for human populations from blood-group gene frequencies. The most important development was their introduction of statistical methods of estimation applied to stochastic models of evolution.

Postal survey of physicians and laboratories: practices and perceptions of molecular oncology testing.

BACKGROUND: Molecular oncology testing (MOT) to detect genomic alterations underlying cancer holds promise for improved cancer care. Yet knowledge limitations regarding the delivery of testing services may constrain the translation of scientific advancements into effective health care. METHODS: We conducted a cross-sectional, self-administered, postal survey of active cancer physicians in Ontario, Canada (N = 611) likely to order MOT, and cancer laboratories (N = 99) likely to refer (i.e., referring laboratories) or conduct (i.e., testing laboratories) MOT in 2006, to assess respondents' perceptions of the importance and accessibility of MOT and their preparedness to provide it. RESULTS: 54% of physicians, 63% of testing laboratories and 60% of referring laboratories responded. Most perceived MOT to be important for treatment, diagnosis or prognosis now, and in 5 years (61% - 100%). Yet only 45% of physicians, 59% of testing labs and 53% of referring labs agreed that patients in their region were receiving MOT that is indicated as a standard of care. Physicians and laboratories perceived various barriers to providing MOT, including, among 70% of physicians, a lack of clear guidelines regarding clinical indications, and among laboratories, a lack of funding (73% - 100%). Testing laboratories were confident of their ability to determine whether and which MOT was indicated (77% and 82% respectively), and perceived that key elements of formal and continuing education were helpful (75% - 100%). By contrast, minorities of physicians were confident of their ability to assess whether and which MOT was indicated (46% and 34% respectively), and while majorities considered various continuing educational resources helpful (68% - 75%), only minorities considered key elements of formal education helpful in preparing for MOT (17% - 43%). CONCLUSION: Physicians and laboratory professionals were enthusiastic about the value of MOT for cancer care but most did not believe patients were gaining adequate access to clinically necessary testing. Further, our results suggest that many were ill equipped as individual stakeholders, or as a coordinated system of referral and interpretation, to provide MOT. These challenges should inspire educational, training and other interventions to ensure that developments in molecular oncology can result in optimal cancer care.

Evaluación etiológica del retardo mental de origen genético: algoritmo diagnóstico y nuevas técnicas moleculares / Etiologic evaluation of genetically caused mental retardation: diagnostic alrithm and new molecular techiques

El retardo mental afecta al 1-3 por ciento de la población. Su etiología es heterogénea, se deben a factores genéticos. El objetivo del artículo es informar sobre etiologías del retardo mental; actualizar sobre las nuevas tecnologías de diagnóstico molecular y entender sus limitaciones para un uso adecuado y racional. Finalmente, se sugiere un algoritmo orientado desde la genética para el estudio del retardo mental; se informa sobre las técnicas disponibles en el país y las que se realizan en países desarrollados. Determinar la etiología permitirá el manejo adecuado del niño y efectuar un correcto asesoramiento familiar.(AU)