Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data.

MOTIVATION: Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations-such as GC content-and applied in single samples separately. The main problem is that not all biases are known. RESULTS: We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xß, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xß as a bilinear model with both X and ß unknown. Joint estimation of X and ß is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and ß. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets. AVAILABILITY AND IMPLEMENTATION: The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The transcriptome-wide landscape of molecular subtype-specific mRNA expression profiles in acute myeloid leukemia.

Molecular classification of acute myeloid leukemia (AML) aids prognostic stratification and clinical management. Our aim in this study is to identify transcriptome-wide mRNAs that are specific to each of the molecular subtypes of AML. We analyzed RNA-sequencing data of 955 AML samples from three cohorts, including the BeatAML project, the Cancer Genome Atlas, and a cohort of Swedish patients to provide a comprehensive transcriptome-wide view of subtype-specific mRNA expression. We identified 729 subtype-specific mRNAs, discovered in the BeatAML project and validated in the other two cohorts. Using unique proteomics data, we also validated the presence of subtype-specific mRNAs at the protein level, yielding a rich collection of potential protein-based biomarkers for the AML community. To enable the exploration of subtype-specific mRNA expression by the broader scientific community, we provide an interactive resource to the public.

A fast detection of fusion genes from paired-end RNA-seq data.

BACKGROUND: Fusion genes are known to be drivers of many common cancers, so they are potential markers for diagnosis, prognosis or therapy response. The advent of paired-end RNA sequencing enhances our ability to discover fusion genes. While there are available methods, routine analyses of large number of samples are still limited due to high computational demands. RESULTS: We develop FuSeq, a fast and accurate method to discover fusion genes based on quasi-mapping to quickly map the reads, extract initial candidates from split reads and fusion equivalence classes of mapped reads, and finally apply multiple filters and statistical tests to get the final candidates. We apply FuSeq to four validated datasets: breast cancer, melanoma and glioma datasets, and one spike-in dataset. The results reveal high sensitivity and specificity in all datasets, and compare well against other methods such as FusionMap, TRUP, TopHat-Fusion, SOAPfuse and JAFFA. In terms of computational time, FuSeq is two-fold faster than FusionMap and orders of magnitude faster than the other methods. CONCLUSIONS: With this advantage of less computational demands, FuSeq makes it practical to investigate fusion genes in large numbers of samples. FuSeq is implemented in C++ and R, and available at https://github.com/nghiavtr/FuSeq for non-commercial uses.

Population structure and genome characterization of local pig breeds in Russia, Belorussia, Kazakhstan and Ukraine.

BACKGROUND: It is generally accepted that domestication of pigs took place in multiple locations across Eurasia; the breeds that originated in Europe and Asia have been well studied. However, the genetic structure of pig breeds from Russia, Belorussia, Kazakhstan and Ukraine, which represent large geographical areas and diverse climatic zones in Eurasia, remains largely unknown. RESULTS: This study provides the first genomic survey of 170 pigs representing 13 breeds from Russia, Belorussia, Kazakhstan and Ukraine; 288 pigs from six Chinese and seven European breeds were also included for comparison. Our findings show that the 13 novel breeds tested derived mainly from European pigs through the complex admixture of Large White, Landrace, Duroc, Hampshire and other breeds, and that they display no geographic structure based on genetic distance. We also found a considerable Asian contribution to the miniature Siberian pigs (Minisib breed) from Russia. Apart from the Minisib, Urzhum, Ukrainian Spotted Steppe and Ukrainian White Steppe breeds, which may have undergone intensive inbreeding, the breeds included in this study showed relatively high genetic diversity and low levels of homozygosity compared to the Chinese indigenous pig breeds. CONCLUSIONS: This study provides the first genomic overview of the population structure and genetic diversity of 13 representative pig breeds from Russia, Belorussia, Kazakhstan and Ukraine; this information will be useful for the preservation and management of these breeds.

Single extracellular vesicle surface protein-based blood assay identifies potential biomarkers for detection and screening of five cancers.

Extracellular vesicles (EVs) and EV proteins are promising biomarkers for cancer liquid biopsy. Herein, we designed a case-control study involving 100 controls and 100 patients with esophageal, stomach, colorectal, liver, or lung cancer to identify common and type-specific biomarkers of plasma-derived EV surface proteins for the five cancers. EV surface proteins were profiled using a sequencing-based proximity barcoding assay. In this study, five differentially expressed proteins (DEPs) and eight differentially expressed protein combinations (DEPCs) showed promising performance (area under curve, AUC > 0.900) in pan-cancer identification [e.g., TENM2 (AUC = 0.982), CD36 (AUC = 0.974), and CD36-ITGA1 (AUC = 0.971)]. Our classification model could properly discriminate between cancer patients and controls using DEPs (AUC = 0.981) or DEPCs (AUC = 0.965). When distinguishing one cancer from the other four, the accuracy of the classification model using DEPCs (85-92%) was higher than that using DEPs (78-84%). We validated the performance in an additional 14 cancer patients and 14 controls, and achieved an AUC value of 0.786 for DEPs and 0.622 for DEPCs, highlighting the necessity to recruit a larger cohort for further validation. When clustering EVs into subpopulations, we detected cluster-specific proteins highly expressed in immune-related tissues. In the context of colorectal cancer, we identified heterogeneous EV clusters enriched in cancer patients, correlating with tumor initiation and progression. These findings provide epidemiological and molecular evidence for the clinical application of EV proteins in cancer prediction, while also illuminating their functional roles in cancer physiopathology.

A nasal cell atlas reveals heterogeneity of tuft cells and their role in directing olfactory stem cell proliferation.

The olfactory neuroepithelium serves as a sensory organ for odors and forms part of the nasal mucosal barrier. Olfactory sensory neurons are surrounded and supported by epithelial cells. Among them, microvillous cells (MVCs) are strategically positioned at the apical surface, but their specific functions are enigmatic, and their relationship to the other specialized epithelial cells is unclear. Here, we establish that the family of MVCs comprises tuft cells and ionocytes in both mice and humans. Integrating analysis of the respiratory and olfactory epithelia, we define the distinct receptor expression of TRPM5+ tuft-MVCs compared with Gɑ-gustducinhigh respiratory tuft cells and characterize a previously undescribed population of glandular DCLK1+ tuft cells. To establish how allergen sensing by tuft-MVCs might direct olfactory mucosal responses, we used an integrated single-cell transcriptional and protein analysis. Inhalation of Alternaria induced mucosal epithelial effector molecules including Chil4 and a distinct pathway leading to proliferation of the quiescent olfactory horizontal basal stem cell (HBC) pool, both triggered in the absence of olfactory apoptosis. Alternaria- and ATP-elicited HBC proliferation was dependent on TRPM5+ tuft-MVCs, identifying these specialized epithelial cells as regulators of olfactory stem cell responses. Together, our data provide high-resolution characterization of nasal tuft cell heterogeneity and identify a function of TRPM5+ tuft-MVCs in directing the olfactory mucosal response to allergens.

Quantification of mutant-allele expression at isoform level in cancer from RNA-seq data.

Even though the role of DNA mutations in cancer is well recognized, current quantification of the RNA expression, performed either at gene or isoform level, typically ignores the mutation status. Standard methods for estimating allele-specific expression (ASE) consider gene-level expression, but the functional impact of a mutation is best assessed at isoform level. Hence our goal is to quantify the mutant-allele expression at isoform level. We have developed and implemented a method, named MAX, for quantifying mutant-allele expression given a list of mutations. For a gene of interest, a mutant reference is constructed by incorporating all possible mutant versions of the wild-type isoforms in the transcriptome annotation. The mutant reference is then used for the RNA-seq reads mapping, which in principle works similarly for any quantification tool. We apply an alternating EM algorithm to the read-count data from the mapping step. In a simulation study, MAX performs well against standard isoform-quantification methods. Also, MAX achieves higher accuracy than conventional gene-based ASE methods such as ASEP. An analysis of a real dataset of acute myeloid leukemia reveals a subgroup of NPM1-mutated patients responding well to a kinase inhibitor. Our findings indicate that quantification of mutant-allele expression at isoform level is feasible and has potential added values for assessing the functional impact of DNA mutations in cancers.

Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients.

Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15-30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.

Synergistically Boosting Thermoelectric Performance of PEDOT:PSS/SWCNT Composites via the Ion-Exchange Effect and Promoting SWCNT Dispersion by the Ionic Liquid.

Poly(3,4 ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is perhaps the most successful polymer material for thermoelectric (TE) applications. So far, treatments by high-boiling solvents, acid or base, or mixing with the carbon nanotube (CNT) are the main ways to improve its TE performance. Herein, we report the synergistically boosting TE properties of PEDOT:PSS/single-walled CNT (SWCNT) composites by the ionic liquid (IL). The composites are prepared by physically mixing the SWCNT dispersion containing the IL with PEDOT:PSS solution and subsequent vacuum filtration. The IL additive has two major functions, that is, inducing the phase separation of PEDOT:PSS and a linear quinoid conformation of PEDOT and promoting the SWCNT dispersion. The maximum power factor at room temperature reaches 182.7 ± 9.2 µW m-1 K-2 (the corresponding electrical conductivity and Seebeck coefficient are 1602.6 ± 103.0 S cm-1 and 33.4 ± 0.4 µV K-1, respectively) for the free-standing flexible film of the PEDOT:PSS/SWCNT composites with the IL, which is much higher than those of the pristine PEDOT:PSS, the IL-free PEDOT:PSS/SWCNT, and the SWCNT films. The high TE performance of composites can be ascribed to synergistic roles of the ion-exchange effect and promotion of SWCNT dispersion by the IL. This work demonstrates the dual roles for the IL in regulating each component of the PEDOT:PSS/SWCNT composite that synergistically boosts the TE performance.

Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing.

Detection of differentially expressed genes is a common task in single-cell RNA-seq (scRNA-seq) studies. Various methods based on both bulk-cell and single-cell approaches are in current use. Due to the unique distributional characteristics of single-cell data, it is important to compare these methods with rigorous statistical assessments. In this study, we assess the reproducibility of 9 tools for differential expression analysis in scRNA-seq data. These tools include four methods originally designed for scRNA-seq data, three popular methods originally developed for bulk-cell RNA-seq data but have been applied in scRNA-seq analysis, and two general statistical tests. Instead of comparing the performance across all genes, we compare the methods in terms of the rediscovery rates (RDRs) of top-ranked genes, separately for highly and lowly expressed genes. Three real and one simulated scRNA-seq data sets are used for the comparisons. The results indicate that some widely used methods, such as edgeR and monocle, have worse RDR performances compared to the other methods, especially for the top-ranked genes. For highly expressed genes, many bulk-cell-based methods can perform similarly to the methods designed for scRNA-seq data. But for the lowly expressed genes performance varies substantially; edgeR and monocle are too liberal and have poor control of false positives, while DESeq2 is too conservative and consequently loses sensitivity compared to the other methods. BPSC, Limma, DEsingle, MAST, t-test and Wilcoxon have similar performances in the real data sets. Overall, the scRNA-seq based method BPSC performs well against the other methods, particularly when there is a sufficient number of cells.

Genome-wide association and evolutionary analyses reveal the formation of swine facial wrinkles in Chinese Erhualian pigs.

Wrinkles are uneven concave-convex folds, ridges or creases in skin. Facial wrinkles appear in head, typically increasing along with aging. However in several Chinese indigenous pigs, such as Erhualian pigs, rich facial wrinkles have been generated during the growth stages as one of their breed characteristics. To investigate the genetic basis underlying the development of swine facial wrinkles, we estimated the folding extent of facial wrinkles in a herd of Erhualian pigs (n=332), and then conducted genome-wide association studies and multi-trait meta-analysis for facial wrinkles using 60K porcine chips. We found that facial wrinkles had high heritability estimates of ~0.7 in Erhualian pigs. Notably, only one genome-wide significant QTL was detected at 34.8 Mb on porcine chromosome 7. The most significant SNP rs80983858 located at the 3255-bp downstream of candidate gene GRM4, and the G allele was of benefit to increase facial wrinkles. Evolutionary and selection analyses suggested that the haplotypes containing G allele were under artificial selection, which was consistent with local animal sacrificial custom praying for longevity. Our findings made important clues for further deciphering the molecular mechanism of swine facial wrinkles formation, and shed light on the research of skin wrinkle development in human or other mammals.

Accumulation of potential driver genes with genomic alterations predicts survival of high-risk neuroblastoma patients.

BACKGROUND: Neuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge. RESULTS: We integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008). CONCLUSIONS: Integration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy. REVIEWERS: This article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca.

Possible introgression of the VRTN mutation increasing vertebral number, carcass length and teat number from Chinese pigs into European pigs.

Vertnin (VRTN) variants have been associated with the number of thoracic vertebrae in European pigs, but the association has not been evidenced in Chinese indigenous pigs. In this study, we first performed a genome-wide association study in Chinese Erhualian pigs using one VRTN candidate causative mutation and the Illumina Porcine 60K SNP Beadchips. The VRTN mutation is significantly associated with thoracic vertebral number in this population. We further show that the VRTN mutation has pleiotropic and desirable effects on teat number and carcass (body) length across four diverse populations, including Erhualian, White Duroc × Erhualian F2 population, Duroc and Landrace pigs. No association was observed between VRTN genotype and growth and fatness traits in these populations. Therefore, testing for the VRTN mutation in pig breeding schemes would not only increase the number of vertebrae and nipples, but also enlarge body size without undesirable effects on growth and fatness traits, consequently improving pork production. Further, by using whole-genome sequence data, we show that the VRTN mutation was possibly introgressed from Chinese pigs into European pigs. Our results provide another example showing that introgressed Chinese genes greatly contributed to the development and production of modern European pig breeds.

Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing.

Domestic pigs have evolved genetic adaptations to their local environmental conditions, such as cold and hot climates. We sequenced the genomes of 69 pigs from 15 geographically divergent locations in China and detected 41 million variants, of which 21 million were absent from the dbSNP database. In a genome-wide scan, we identified a set of loci that likely have a role in regional adaptations to high- and low-latitude environments within China. Intriguingly, we found an exceptionally large (14-Mb) region with a low recombination rate on the X chromosome that appears to have two distinct haplotypes in the high- and low-latitude populations, possibly underlying their adaptation to cold and hot environments, respectively. Surprisingly, the adaptive sweep in the high-latitude regions has acted on DNA that might have been introgressed from an extinct Sus species. Our findings provide new insights into the evolutionary history of pigs and the role of introgression in adaptation.