Search

Motivation Molecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carcinogenicity information is limited and building data-driven models with good prediction accuracy remains a major challenge. Results In this work, we propose CONCERTO, a deep learning model that uses a graph transformer in conjunction with a molecular fingerprint representation for carcinogenicity prediction from molecular structure. Special efforts have been made to overcome the data size constraint, such as multi-round pre-training on related but lower quality mutagenicity data, and transfer learning from a large self-supervised model. Extensive experiments demonstrate that our model performs well and can generalize to external validation sets. CONCERTO could be useful for guiding future carcinogenicity experiments and provide insight into the molecular basis of carcinogenicity. Availability and implementation The code and data underlying this article are available on github at https://github.com/bowang-lab/CONCERTO

Molecular carcinogenicity is a preventable cause of cancer, however, most experimental testing of molecular compounds is an expensive and time consuming process, making high throughput experimental approaches infeasible. In recent years, there has been substantial progress in machine learning techniques for molecular property prediction. In this work, we propose a model for carcinogenicity prediction, CONCERTO, which uses a graph transformer in conjunction with a molecular fingerprint representation, trained on multi-round muta-genicity and carcinogenicity objectives. To train and validate CONCERTO, we augment the training dataset with more informative labels and utilize a larger external validation dataset. Extensive experiments demonstrate that our model yields results superior to alternate approaches for molecular carcinogenicity prediction.

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities and/or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we have developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search and then build a targeted database for refined search. Evaluated on three representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 80% comparing to conventional searches with unspecific digestions while maintaining a very high accuracy (∼96%) as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data so that it performs equally well on both well-studied and poorly-studied HLA types, unlike a previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to conventional database search engines. Finally, we demonstrate the practical value of IntroSpect by discovering neoantigens from MS data directly. IntroSpect is freely available at https://github.com/BGI2016/IntroSpect.

Cardiac injury and dysfunction occur in COVID-19 patients and increase the risk of mortality. Causes are ill defined, but could be direct cardiac infection and/or inflammation-induced dysfunction. To identify mechanisms and cardio-protective drugs, we use a state-of-the-art pipeline combining human cardiac organoids with phosphoproteomics and single nuclei RNA sequencing. We identify an inflammatory ‘cytokine-storm’, a cocktail of interferon gamma, interleukin 1β and poly(I:C), induced diastolic dysfunction. Bromodomain-containing protein 4 is activated along with a viral response that is consistent in both human cardiac organoids and hearts of SARS-CoV-2 infected K18-hACE2 mice. Bromodomain and extraterminal family inhibitors (BETi) recover dysfunction in hCO and completely prevent cardiac dysfunction and death in a mouse cytokine-storm model. Additionally, BETi decreases transcription of genes in the viral response, decreases ACE2 expression and reduces SARS-CoV-2 infection of cardiomyocytes. Together, BETi, including the FDA breakthrough designated drug apabetalone, are promising candidates to prevent COVID-19 mediated cardiac damage., A combination of phosphoproteomics, drug screening and single-cell sequencing approaches identifies how cytokines elevated in COVID-19 patients drives cardiac dysfunction, with BET inhibitors serving as potential lead candidates decrease ACE2 cardiac expression and infection.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

SUMMARYCardiac injury and dysfunction occur in COVID-19 patients and increase the risk of mortality. Causes are ill defined, but could be direct cardiac infection and/or inflammation-induced dysfunction. To identify mechanisms and cardio-protective drugs, we use a state-of-the-art pipeline combining human cardiac organoids with phosphoproteomics and single nuclei RNA sequencing. We identify an inflammatory ‘cytokine-storm’, a cocktail of interferon gamma, interleukin 1β and poly(I:C), induced diastolic dysfunction. Bromodomain-containing protein 4 is activated along with a viral response that is consistent in both human cardiac organoids and hearts of SARS-CoV-2 infected K18-hACE2 mice. Bromodomain and extraterminal family inhibitors (BETi) recover dysfunction in hCO and completely prevent cardiac dysfunction and death in a mouse cytokine-storm model. Additionally, BETi decreases transcription of genes in the viral response, decreases ACE2 expression and reduces SARS-CoV-2 infection of cardiomyocytes. Together, BETi, including the FDA breakthrough designated drug apabetalone, are promising candidates to prevent COVID-19 mediated cardiac damage.

MotivationHi-C data has enabled the genome-wide study of chromatin folding and architecture, and has led to important discoveries in the structure and function of chromatin conformation. Here, high resolution data plays a particularly important role as many chromatin substructures such as Topologically Associating Domains (TADs) and chromatin loops cannot be adequately studied with low resolution contact maps. However, the high sequencing costs associated with the generation of high resolution Hi-C data has become an experimental barrier. Data driven machine learning models, which allow low resolution Hi-C data to be computationally enhanced, offer a promising avenue to address this challenge.ResultsBy carefully examining the properties of Hi-C maps and integrating various recent advances in deep learning, we developed a Hi-C Super-Resolution (HiCSR) framework capable of accurately recovering the fine details, textures, and substructures found in high resolution contact maps. This was achieved using a novel loss function tailored to the Hi-C enhancement problem which optimizes for an adversarial loss from a Generative Adversarial Network (GAN), a feature reconstruction loss derived from the latent representation of a denoising autoencoder, and a pixel-wise loss. Not only can the resulting framework generate enhanced Hi-C maps more visually similar to the original high resolution maps, it also excels on a suite of reproducibility metrics produced by members of the ENCODE Consortium compared to existing approaches, including HiCPlus, HiCNN, hicGAN and DeepHiC. Finally, we demonstrate that HiCSR is capable of enhancing Hi-C data across sequencing depth, cell types, and species, recovering biologically significant contact domain boundaries.AvailabilityWe make our implementation available for download at: https://github.com/PSI-Lab/HiCSRContactljlee@psi.toronto.eduSupplementary informationAvailable Online

// Lihua Peng 1, 2, * , Leo J Lee 2, 3, * , Heng Xiong 2 , Hong Su 2 , Junhua Rao 2 , Dakai Xiao 4, 5, 6 , Jianxing He 4, 5, 7 , Kui Wu 2, 8 , Dongbing Liu 2 1 BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China 2 BGI-Shenzhen, Shenzhen 518083, China 3 Department of Electrical and Computer Engineering, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3G4, Canada 4 Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China 5 Guangzhou Institute of Respiratory Disease & State Key Laboratory of Respiratory Disease, Guangzhou 510120, China 6 Research Center for Translational Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China 7 National Clinical Research Center for Respiratory Disease, Guangzhou 510120, China 8 Department of Biology, University of Copenhagen, Copenhagen N DK-2200, Denmark * These authors have contributed equally to this work Correspondence to: Dongbing Liu, email: liudongbing@genomics.cn Keywords: lung adenocarcinoma, RNA editing, hyper-editing, primary, metastatic Received: May 27, 2016 Accepted: November 21, 2016 Published: December 21, 2016 ABSTRACT RNA editing results in post-transcriptional modification and could potentially contribute to carcinogenesis. However, RNA editing in advanced lung adenocarcinomas has not yet been studied. Based on whole genome and transcriptome sequencing data, we identified 1,071,296 RNA editing events from matched normal, primary and metastatic samples contributed by 24 lung adenocarcinoma patients, with 91.3% A-to-G editing on average, and found significantly more RNA editing sites in tumors than in normal samples. To investigate cancer relevant editing events, we detected 67,851 hyper-editing sites in primary and 50,480 hyper-editing sites in metastatic samples. 46 genes with hyper-editing in coding regions were found to result in amino acid alterations, while hundreds of hyper-editing events in non-coding regions could modulate splicing or gene expression, including genes related to tumor stage or clinic prognosis. Comparing RNA editome of primary and metastatic samples, we also discovered hyper-edited genes that may promote metastasis development. These findings showed a landscape of RNA editing in matched normal, primary and metastatic tissues of lung adenocarcinomas for the first time and provided new insights to understand the molecular characterization of this disease.

RNA editing of adenosine to inosine (A to I) is catalyzed by ADAR1 and dramatically alters the cellular transcriptome, although its functional roles in somatic cell reprogramming are largely unexplored. Here, we show that loss of ADAR1-mediated A-to-I editing disrupts mesenchymal-to-epithelial transition (MET) during induced pluripotent stem cell (iPSC) reprogramming and impedes acquisition of induced pluripotency. Using chemical and genetic approaches, we show that absence of ADAR1-dependent RNA editing induces aberrant innate immune responses through the double-stranded RNA (dsRNA) sensor MDA5, unleashing endoplasmic reticulum (ER) stress and hindering epithelial fate acquisition. We found that A-to-I editing impedes MDA5 sensing and sequestration of dsRNAs encoding membrane proteins, which promote ER homeostasis by activating the PERK-dependent unfolded protein response pathway to consequently facilitate MET. This study therefore establishes a critical role for ADAR1 and its A-to-I editing activity during cell fate transitions and delineates a key regulatory layer underlying MET to control efficient reprogramming.

Background Accurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS) profiling of eluted HLA ligands, which provides unbiased, high-throughput measurements of HLA associated peptides in vivo , could be used to faithfully model the presentation of epitopes on the cell surface. In addition, gene expression profiles measured by RNA-seq data in a specific cell/tissue type can significantly improve the performance of epitope presentation prediction. However, although large amount of high-quality MS data of HLA-bound peptides is being generated in recent years, few provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. Methods We collected publicly available HLA peptidome and matching RNA-seq data of 34 cell lines derived from various sources. We built position score specific matrixes (PSSMs) for 21 HLA-I alleles based on these MS data, then used logistic regression (LR) to model the relationship among PSSM score, gene expression and peptide length to predict whether a peptide could be presented in each of the cell line. Comparing the feature weights and biases across different HLA-I alleles and cell lines, we observed a universal relationship among these three variables. To confirm this, we built a single LR model by pooling PSSM scores, gene expression levels and peptide length features across different HLA alleles and cell lines, and compared its performance with the allele and cell line specific LR models. Indeed, the predictive powers had no significant differences across cell lines and HLA alleles, and both substantially outperformed predictions based on PSSM scores alone. Based on such a finding, we further built a universal LR model, termed Epitope Presentation Integrated prediCtion (EPIC), based on more than 180,000 unique HLA ligands collected from public sources and ∼3,000 HLA ligands generated by ourselves, to predict epitope presentation for 66 common HLA-I alleles. Results When evaluating EPIC on large, independent HLA eluted ligand datasets, it performed substantially better than other popular methods, including MixMHCpred (v2.0), NetMHCpan (v4.0), and MHCflurry (v1.2.2), with an average 0.1% positive predictive value (PPV) of 51.59%, compared to 36.98%, 36.41%, 24.67% and 23.39% achieved by MixMHCpred, NetMHCpan-4.0 (EL), NetMHCpan-4.0 (BA) and MHCflurry, respectively. It is also comparable to EDGE, a recent deep learning-based model that is not yet publicly available, on predicting epitope presentation and selecting immunogenic cancer neoantigens. However, the simplicity and flexibility of EPIC makes it much easier to be applied in diverse situations, especially when users would like to take advantage of emerging eluted ligand data for new HLA alleles. We demonstrated this by generating MS data for the HCC4006 cell line and adding the support of HLA-A*33:03, which has no previous MS or binding affinity data available, to EPIC. EPIC is publicly available at < https://github.com/BGI2016/EPIC >. Conclusions we have developed an easy to use, publicly available epitope prediction tool, EPIC, that incorporates information from both MS and RNA-seq data, and demonstrated its superior performance over existing public methods.

RNA chemical modifications are intricately linked to transcriptome structural and functional diversity. Whereas somatic cell reprogramming involves a profound rewiring of epigenetic and transcriptomic landscapes, how RNA modifications contribute to overriding somatic cell identity is incompletely understood. Here we demonstrate that loss of Adenosine-to-Inosine (A-to-I) editing of RNA by ADAR1 hampers mesenchymal-to-epithelial transition (MET) and impedes induced pluripotent stem cell (iPSC) formation. Absence of RNA editing leads to aberrant innate immune response (IIR) expression programs, which unleash endoplasmic reticulum (ER) stress and hinder epithelial fate acquisition. Using chemical and genetic approaches, we show a dual role for A-to-I editing in preventing MDA5-dependent activation of ER stress and ensuring proper double strand RNA (dsRNA) compartmentalization for PERK activation. Thus, our study establishes a critical role for A-to-I RNA modification in cell fate transitions during reprogramming, which delineates a novel regulatory layer underlying MET control for efficient reprogramming.

MotivationDetermining RNA binding protein(RBP) binding specificity is crucial for understanding many cellular processes and genetic disorders. RBP binding is known to be affected by both the sequence and structure of RNAs. Deep learning can be used to learn generalizable representations of raw data and has improved state of the art in several fields such as image classification, speech recognition and even genomics. Previous work on RBP binding has either used shallow models that combine sequence and structure or deep models that use only the sequence. Here we combine both abilities by augmenting and refining the original Deepbind architecture to capture structural information and obtain significantly better performance.ResultsWe propose two deep architectures, one a lightweight convolutional network for transcriptome wide inference and another a Long Short-Term Memory(LSTM) network that is suitable for small batches of data. We incorporate computationally predicted secondary structure features as input to our models and show its effectiveness in boosting prediction performance. Our models achieved significantly higher correlations on held out in-vitro test data compared to previous approaches, and generalise well to in-vivo CLIP-SEQ data achieving higher median AUCs than other approaches. We analysed the output from our model for VTS1 and CPO and provided intuition into its working. Our models confirmed known secondary structure preferences for some proteins as well as found new ones where secondary structure might play a role. We also demonstrated the strengths of our model compared to other approaches such as the ability to combine information from long distances along the input.AvailabilitySoftware and models are available at https://github.com/shreshthgandhi/cDeepbindContactljlee@psi.toronto.edu, frey@psi.toronto.edu

With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions so that its biochemical mechanisms and functional roles can be further understood. However, a major barrier that prevents RNA editing from being a routine RNA-seq analysis, similar to gene expression and splicing analysis, for example, is the lack of user-friendly and effective computational tools. Based on years of experience of analyzing RNA editing using diverse RNA-seq datasets, we have developed a software tool, RED-ML: RNA Editing Detection based on Machine learning (pronounced as "red ML"). The input to RED-ML can be as simple as a single BAM file, while it can also take advantage of matched genomic variant information when available. The output not only contains detected RNA editing sites, but also a confidence score to facilitate downstream filtering. We have carefully designed validation experiments and performed extensive comparison and analysis to show the efficiency and effectiveness of RED-ML under different conditions, and it can accurately detect novel RNA editing sites without relying on curated RNA editing databases. We have also made this tool freely available via GitHub . We have developed a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. With the availability of RED-ML, it is now possible to conveniently make RNA editing a routine analysis of RNA-seq. We believe this can greatly benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing posttranscriptional modification process.

Background: Accurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS)profiling of eluted HLA ligands, which provides unbiased, high-throughput measurements of HLA associated peptides resulting from in vivo cellular processing, can be a highly valuable training set to build predictive models of HLA binding. In addition, gene expression profiles measured by RNA-seq data in a specific cell type could significantly improve the positive predictive value (PPV) of epitope presentation prediction. Although large amount of high-quality mass spectrometry data of HLA-bound peptides is being generated in the last few years, few of them provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. Here, we aim to develop a publicly available prediction tool incorporating both sources of information, and demonstrate its superior performance over existing methods. Methods: We obtained public HLA peptidome datasets with matching RNA-seq data of twelve cell lines derived from multiple tissues. We used these MS HLA ligand data to build Position Score Specific Matrixes (PSSMs) for five HLA-I alleles across these cell lines. We then used logistic regression to model the relationship among PSSM score, gene expression, peptide length distribution and whether the peptide could be presented in each of the twelve cell lines, and compared the feature weights among them. Results: We found that the feature weights across different HLA-I alleles and cell lines were close to each other, suggesting that there is a universal relationship between PSSM score and gene expression across different cell lines that could be applied to epitope presentation prediction for multiple alleles in diverse tissues. When we replaced the cell-line-specific weights with universal weights summarized from all the cell lines, the logistic regression model’s predicted power for each cell line only dropped slightly and still substantially outperformed predictions based on PSSM scores alone. Based on such a finding, we applied the universal feature weights to more than 180,000 unique HLA ligands collected from public HLA peptidomics datasets, and presented an Epitope Presentation Integrated prediCtion (EPIC) model for 66 HLA alleles. EPIC was substantially better than other popular methods, including MixMHCpred, NetMHCpan (v4.0), and MHCflurry, when evaluated on independent HLA eluted ligand datasets, with an average 0.1%PPV of 53.58%, compared to 40.50%, 40.20%, 29.81%, and 25.57% achieved by MixMHCpred, NetMHCpan (EL), NetMHCpan (BA), and MHCflurry, respectively. Conclusion: By integrating MS and expression data, EPIC is superior to currently available methods in predicting epitope presentation for the 66 common HLA alleles that our models were built on. Citation Format: Weipeng Hu, Si Qiu, Youping Li, Geng Liu, Xiuqing Zhang, Leo J Lee. EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3383.

When estimating expression of a transcript or part of a transcript using RNA-seq data, it is commonly assumed that reads are generated uniformly from positions within the transcript. While this assumption is acceptable for long transcript sequences where reads from many positions are averaged, it frequently leads to large errors for short sequences, e.g., less than 100 bp. Analysis of short sequences, such as when studying splice junctions and microRNAs, is increasingly important and necessitates addressing errors in short-sequence expression estimation. Indeed, when we examined RNA-seq data from diverse studies, we found that large errors are introduced by variations in RNA-seq coverage due to sequence content, experimental conditions and sample preparation.We developed a technique that we call the positional bootstrap, which quantifies the level of uncertainty in expression induced by non-uniform coverage. Unlike methods that attempt to correct for biases in coverage, but do so by making strong assumptions about the form of those biases, the positional bootstrap can quantify the noise induced by all types of bias, including unknown ones. Results obtained using independently generated RNA-seq datasets show that the positional bootstrap increases the accuracy of estimates of alternative splicing levels, tissue-differential alternative splicing and tissue differential expression, by a factor of up to 10.A Python implementation of the algorithm to quantify splicing levels is freely available from github.com/PSI-Lab/BENTO-Seq.

Alternative splicing (AS) is the process by which multiple mRNA transcripts are produced from a single precursor transcript through the differential utilization of splice sites. Alternative splicing is one of the key mechanisms that have evolved in metazoans to generate increased transcriptome complexity and recent studies estimate that greater than 95% of human multi-exon genes express multiple splice isoforms (Pan et al. 2008; Wang et al. 2008). Moreover, alternatively spliced exons are often differentially regulated across tissues and during development, suggesting that individual isoforms may serve specific spatial or temporal roles (Hartmann and Valcarcel 2009; Licatalosi and Darnell 2010; Nilsen and Graveley 2010). The importance of proper regulation of AS during development has been demonstrated in many different instances; one particularly well-studied example is that of the sex determination pathway in Drosophila. In this pathway, the female-specific expression of a splicing regulator transformer stimulates the inclusion of exons in transcripts of the doublesex and fruitless transcription factor genes (Lopez 1998; Forch and Valcarcel 2003). The female-specific isoforms of these transcription factors subsequently activate the expression of genes required for female development, while the male-specific variants induce a gene expression program important for male differentiation (Dulac 2005; Shirangi and McKeown 2007). Similar spatio-temporally regulated AS networks are likely to exist in metazoans. The characterization of these AS networks, and their integration with other layers of gene regulation, will be necessary for a more complete understanding of development (Blencowe 2006). The nematode C. elegans, with its amenability for genetic manipulations, defined cell lineage, simple but extensively differentiated nervous system, conserved splicing machinery, and established molecular and cell biological resources, provides an attractive yet relatively under-explored model organism with which to study the regulation and functions of AS in vivo. Tissue or developmental stage-specific AS events have been identified in approximately 70 C. elegans genes which, like those identified in vertebrates, are controlled by combinatorial interactions between splicing factors (Kuroyanagi et al. 2007; Barberan-Soler and Zahler 2008; Ohno et al. 2008). These studies, while informative, were limited to the analysis of isoforms represented in expressed sequence tag (EST)/cDNA libraries, which currently do not provide a full coverage of the transcriptome. The advent of deep sequencing technologies has now enabled transcriptome analyses at unprecedented levels of sensitivity and precision, and these have recently been applied in C. elegans (Hillier et al. 2009; Ramani et al. 2009; Mangone et al. 2010). However, due in part to limited depth of coverage of existing data sets and the ongoing development of analytical software, it has remained a major challenge to systematically categorize and quantify the relative usage of isoforms across development for both known and novel AS events. In this study, we combined high-throughput sequencing of mRNA (RNA-seq) and quantitative AS microarray profiling to provide a high resolution survey of splicing across C. elegans development. We have identified thousands of new AS events and hundreds of isoforms that are differentially expressed during development. Additionally, we identified candidate cis-elements that likely play a role in regulating these events. To facilitate the use of these data, we have created the “C. elegans Splice Browser,” a web resource in which all of the data in the present study are made accessible. This resource, which represents the most complete set of splice variants reported to date, should serve as a valuable basis for focused analyses of the functional importance of individual splice isoforms in vivo.

A novel Kalman filtering/smoothing algorithm is presented for efficient and accurate estimation of vocal tract resonances or formants, which are natural frequencies and bandwidths of the resonator from larynx to lips, in fluent speech. The algorithm uses a hidden dynamic model, with a state-space formulation, where the resonance frequency and bandwidth values are treated as continuous-valued hidden state variables. The observation equation of the model is constructed by an analytical predictive function from the resonance frequencies and bandwidths to LPC cepstra as the observation vectors. This nonlinear function is adaptively linearized, and a residual or bias term, which is adaptively trained, is added to the nonlinear function to represent the iteratively reduced piecewise linear approximation error. Details of the piecewise linearization design process are described. An iterative tracking algorithm is presented, which embeds both the adaptive residual training and piecewise linearization design in the Kalman filtering/smoothing framework. Experiments on estimating resonances in Switchboard speech data show accurate estimation results. In particular, the effectiveness of the adaptive residual training is demonstrated. Our approach provides a solution to the traditional "hidden formant problem," and produces meaningful results even during consonantal closures when the supra-laryngeal source may cause no spectral prominences in speech acoustics

Predicting defects in RNA splicing Most eukaryotic messenger RNAs (mRNAs) are spliced to remove introns. Splicing generates uninterrupted open reading frames that can be translated into proteins. Splicing is often highly regulated, generating alternative spliced forms that code for variant proteins in different tissues. RNA-binding proteins that bind specific sequences in the mRNA regulate splicing. Xiong et al. develop a computational model that predicts splicing regulation for any mRNA sequence (see the Perspective by Guigó and Valcárcel). They use this to analyze more than half a million mRNA splicing sequence variants in the human genome. They are able to identify thousands of known disease-causing mutations, as well as many new disease candidates, including 17 new autism-linked genes. Science , this issue 10.1126/science.1254806 ; see also p. 124

In their report “The transcriptional landscape of the mammalian genome” (2 Sept. 2005, p. [1559][1]), the RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group) and the FANTOM Consortium claim to have found 5154 new proteins in the mouse genome not encoded by previously known mRNA sequences, which could potentially correspond to a considerable number of new protein-coding genes (4311, following clustering) ([1][2]). This claim contrasts dramatically with the view of the International Human Genome Sequencing Consortium ([2][3]), which estimated that there are 20,000 to 25,000 protein-coding genes. Since there are already 22,287 genes in the Ensembl 34d catalog, this implies 0 to 2713 new genes. RIKEN/FANTOM's estimate contrasts even more strikingly with our results using exon microarrays ([3][4]), in which the number of new multi-exon protein-coding genes was estimated to be at most in the hundreds. We analyzed the putative new FANTOM proteins ([4][5]), first by comparing their sequences with RefSeq release 13 from NCBI, NIH, and including only those transcripts that are linked to a reference published no later than 1 May 2005, thus excluding all the new FANTOM proteins. Restricting our analysis to the transcripts that have strong experimental evidence (labeled Provisional, Validated, or Reviewed), we found that 2917 (56.6%) of the FANTOM proteins are in fact splice isoforms of known RefSeq transcripts, with the majority of them (2716) corresponding to exon-skipping events. By then including predicted RefSeq transcripts (labeled Genome Annotation, Inferred, Model, Predicted) in our analysis, 3568 (69.2%) were found to be splice isoforms of known transcripts. By including GenBank mRNAs linked to publications before 1 May 2005, we found an extra 303 splice isoforms, bringing the total of already-annotated genes to 3871 (75.1%). Moreover, of the 5154 FANTOM proteins, our microarray analysis detected 2293 (by two or more exons), 144 of which are among the remaining 1283 FANTOM proteins and most (131) of which are associated with known genes. We next asked whether the remaining 1193 putative proteins could be accounted for as false detections. The median open reading frame (ORF) size in this set is 119 amino acids (aa), significantly shorter than that of all the FANTOM proteins (330 aa). Although many real proteins have a length less than 119 aa, we hypothesized that such a short ORF length can arise in noncoding transcripts by chance. The FANTOM Consortium identified 23,218 nonoverlapping, noncoding transcripts, so to test this hypothesis we generated a set of 20,000 random cDNAs of 2000 bases (typical gene length) and found that 1247 of them had ORFs of 119 aa or more. Therefore, it is possible that a large portion of the remaining 1193 putative proteins arose at random from noncoding transcripts and may not encode functional polypeptides. On the basis of this analysis, the number of completely new protein-coding genes discovered by the FANTOM Consortium is at most in the hundreds, consistent with current estimates based on both sequence and microarray analysis ([2][3], [3][4]). 1. 1.[↵][6] FANTOM3 cDNA sequences are not provided, but the protein sequences can be downloaded from . 2. 2.[↵][7] Nature (2004) International Human Genome Sequencing Consortium, 431, 931. 3. 3.[↵][8] 1. B. J. Frey 2. et al. , Nat. Genet. (2005) 37, 991. 4. 4.[↵][9] See [www.psi.toronto.edu/TransLand][10] for details. # Response {#article-title-2} Lee et al. point out that the number of reported protein sequences in FANTOM3 that map to new positions on the genome appears to be too large. We are grateful to them for highlighting this discrepancy, which we investigated and thus discovered an error. For a detailed description of the correction, see the Corrections and Clarifications section in this issue. The effect of the error is somewhat less than suggested by Lee et al. In particular, our estimate of the number of new protein-coding genes found by us has been revised from 5154 to 2222, a reduction of more than half, but much less than the order of magnitude suggested by Lee et al. As correctly pointed out, the rest of the 5154 cDNAs are mainly alternatively spliced isoforms. Lee et al. present three forms of evidence: sequence similarity, exon microarray data, and ORF size. (i) The sequence homology data largely reflect the revision to the number that we mention above, except that Lee et al. used a recent RefSeq database, whereas we used Genbank (7 January 2004). There is no evidence that all RefSeq sequences correspond to real transcribed RNAs because they often include ab initio predicted exons ([1][11]). Our strategy was to construct the transcriptional frameworks entirely based on real RNA transcripts, rather than in silico reconstruction of putative gene structures. (ii) The exon microarray data concern less than 3% of the number of discussed proteins and do not have any impact on the global message of a project of the scale of FANTOM3. Despite Frey et al. 's impressive computational reconstruction of gene structure by analyzing expression patterns of ab initio predicted exons ([2][12]), we argue that this does not prove the physical structure of each mRNA and the complexity of the transcriptome with the same resolution achieved by sequencing libraries derived from mRNAs. In fact, our data show that “genes” have multiple starting and termination sites: We have conservatively identified at least 181,000 different RNA transcripts. Additionally, Frey et al. ([2][12]) used only computationally predicted exons. Rare, newly discovered transcripts are unlikely to have been in the training sets of ab initio exon identification tools, and their sensitivity to predict rare transcriptional events is not obvious. (iii) As for ORF size, 119 amino acids is a perfectly respectable size for a protein and within the bounds of statistical variation we expect. In this regard, we have further identified in the FANTOM3 dataset at least 1100 proteins shorter than 100 amino acids ([3][13]). Also, all of the novel FANTOM3 transcripts have been manually curated by individual researchers to distinguish them from novel noncoding RNAs. In any case, our final understanding of the number of protein-coding mRNAs will derive from experimental validation with full-length cDNA clones ([3][13]) rather than computational inferences. We direct interested parties to the relevant section of the FANTOM3 Web site ( ) where the updated files are available, and we thank Lee et al. for helping us to improve and update our analysis. 1. 1.[↵][14] 1. X. Pruitt 2. et al. , Nucleic Acids Res. (2005) 33, D501. 2. 2.[↵][15] 1. B. Frey 2. et al. , Nat. Genet. (2005) 37, 991. 3. 3.[↵][16] 1. M. Frith 2. et al. , Plos Genet., in press. [1]: /lookup/doi/10.1126/science.1112014 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #xref-ref-1-1 "View reference 1. in text" [7]: #xref-ref-2-1 "View reference 2. in text" [8]: #xref-ref-3-1 "View reference 3. in text" [9]: #xref-ref-4-1 "View reference 4. in text" [10]: http://www.psi.toronto.edu/TransLand [11]: #ref-5 [12]: #ref-6 [13]: #ref-7 [14]: #xref-ref-5-1 "View reference 1. in text" [15]: #xref-ref-6-1 "View reference 2. in text" [16]: #xref-ref-7-1 "View reference 3. in text"

Microcephaly-capillary malformation (MIC-CAP) syndrome is characterized by severe microcephaly with progressive cortical atrophy, intractable epilepsy, profound developmental delay and multiple small capillary malformations on the skin. We used whole-exome sequencing of five patients with MIC-CAP syndrome and identified recessive mutations in STAMBP, a gene encoding the deubiquitinating (DUB) isopeptidase STAMBP (STAM-binding protein, also known as AMSH, associated molecule with the SH3 domain of STAM) that has a key role in cell surface receptor-mediated endocytosis and sorting. Patient cell lines showed reduced STAMBP expression associated with accumulation of ubiquitin-conjugated protein aggregates, elevated apoptosis and insensitive activation of the RAS-MAPK and PI3K-AKT-mTOR pathways. The latter cellular phenotype is notable considering the established connection between these pathways and their association with vascular and capillary malformations. Furthermore, our findings of a congenital human disorder caused by a defective DUB protein that functions in endocytosis implicates ubiquitin-conjugate aggregation and elevated apoptosis as factors potentially influencing the progressive neuronal loss underlying MIC-CAP syndrome.

Whence Species Variation? Vertebrates have widely varying phenotypes that are at odds with their much more limited proteincoding genotypes and conserved messenger RNA expression patterns. Genes with multiple exons and introns can undergo alternative splicing, potentially resulting in multiple protein isoforms (see the Perspective by Papasaikas and Valcárcel ). Barbosa-Morais et al. (p. 1587 ) and Merkin et al. (p. 1593 ) analyzed alternative splicing across the genomes of a variety of vertebrates, including human, primates, rodents, opossum, platypus, chicken, lizard, and frog. The findings suggest that the evolution of alternative splicing has for the most part been very rapid and that alternative splicing patterns of most organs more strongly reflect the identity of the species rather than the organ type. Species-classifying alternative splicing can affect key regulators, often in disordered regions of proteins that may influence protein-protein interactions, or in regions involved in protein phosphorylation.

Background: Transcriptome profiling of patterns of RNA expression is a powerful approach to identify networks of genes that play a role in disease. To date, most mRNA profiling of tissues has been accomplished using microarrays, but next-generation sequencing can offer a richer and more comprehensive picture. Methodology/Principal Findings: ECO is a rare multi-system developmental disorder caused by a homozygous mutation in ICK encoding intestinal cell kinase. We performed gene expression profiling using both cDNA microarrays and next-generation mRNA sequencing (mRNA-seq) of skin fibroblasts from ECO-affected subjects. We then validated a subset of differentially expressed transcripts identified by each method using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, we used gene ontology (GO) to identify critical pathways and processes that were abnormal according to each technical platform. Methodologically, mRNA-seq identifies a much larger number of differentially expressed genes with much better correlation to qRT-PCR results than the microarray (r 2 = 0.794 and 0.137, respectively). Biologically, cDNA microarray identified functional pathways focused on anatomical structure and development, while the mRNA-seq platform identified a higher proportion of genes involved in cell division and DNA replication pathways. Conclusions/Significance: Transcriptome profiling with mRNA-seq had greater sensitivity, range and accuracy than the microarray. The two platforms generated different but complementary hypotheses for further evaluation. © 2011 Lahiry et al.

Recent papers have described the first application of high-throughput sequencing (HTS) technologies to the characterization of transcriptomes. These studies emphasize the tremendous power of this new technology, in terms of both profiling coverage and quantitative accuracy. Initial discoveries include the detection of substantial new transcript complexity, the elucidation of binding maps and regulatory properties of RNA-binding proteins, and new insights into the links between different steps in pre-mRNA processing. We review these findings, focusing on results from profiling mammalian transcriptomes. The strengths and limitations of HTS relative to microarray profiling are discussed. We also consider how future advances in HTS technology are likely to transform our understanding of integrated cellular networks operating at the RNA level.

We carried out the first analysis of alternative splicing complexity in human tissues using mRNA-Seq data. New splice junctions were detected in approximately 20% of multiexon genes, many of which are tissue specific. By combining mRNA-Seq and EST-cDNA sequence data, we estimate that transcripts from approximately 95% of multiexon genes undergo alternative splicing and that there are approximately 100,000 intermediate- to high-abundance alternative splicing events in major human tissues. From a comparison with quantitative alternative splicing microarray profiling data, we also show that mRNA-Seq data provide reliable measurements for exon inclusion levels.

A better understanding of cell behavior is very important in drug and disease research. Cell size, shape, and motility may play a key role in stem-cell specialization or cancer development. However the traditional method of inferring these values from image sequences manually is such an onerous task that automated methods of cell tracking and segmentation are in high demanded, especially given the increasing amount of cell data being collected. In this paper, a novel probabilistic cell model is designed to segment the individual hematopoietic stem cells (HSCs) extracted from mice bone marrow cells. The proposed cell model has been successfully applied to HSC segmentation, identifying the most probable cell locations in the image on the basis of cell brightness and morphology.

A novel approach is developed for efficient and accurate tracking of vocal tract resonances, which are natural frequencies of the resonator from larynx to lips, in fluent speech. The tracking algorithm is based on a version of the structured speech model consisting of continuous-valued hidden dynamics and a piecewise-linearized prediction function from resonance frequencies and bandwidths to LPC cepstra. We present details of the piecewise linearization design process and an adaptive training technique for the parameters that characterize the prediction residuals. An iterative tracking algorithm is described and evaluated that embeds both the prediction-residual training and the piecewise linearization design in an adaptive Kalman filtering framework. Experiments on tracking vocal tract resonances in Switchboard speech data demonstrate high accuracy in the results, as well as the effectiveness of residual training embedded in the algorithm. Our approach differs from traditional formant trackers in that it provides meaningful results even during consonantal closures when the supra-laryngeal source may cause no spectral prominences in speech acoustics.

Virtually all implementations of simulated annealing are simplified by assuming discrete unknowns, however continuous-parameter annealing has many potential applications to image processing. Widely scattered problems such as formant tracking, boundary estimation and phase- unwrapping can all be approached as the annealed minimizations of continuous B-spline parameters. The benefits of simulated annealing are well known, including an insensitivity to initial conditions and the ability to solve problems with many local minima. Discrete variable annealing has seen broad application, however continuous-variable annealing is limited by the computational challenge of Gibbs sampling. In this paper we develop efficient approaches to sampling, illustrated in the context of contour tracking in noisy images.

We present a swarm-based, 3-dimensional model of the human immune system and its response to first and second viral antigen exposure. Our model utilizes a decentralized swarm approach with multiple agents acting independently—following local interaction rules—to exhibit complex emergent behaviours, which constitute externally observable and measurable immune reactions. The two main functional branches of the human immune system, humoral and cell-mediated immunity, are simulated. We model the production of antibodies in response to a viral population; antibody-antigen complexes are formed, which are removed by macrophages; virally infected cells are lysed by cytotoxic T cells. Our system also demonstrates reinforced reaction to a previously encountered pathogen, thus exhibiting realistic memory response.

This paper describes novel and powerful variational EM algorithms for the segmental switching state space models used in speech applications, which are capable of capturing key internal (or hidden) dynamics of natural speech production. Hidden dynamic models (HDMs) have recently become a class of promising acoustic models to incorporate crucial speech-specific knowledge and overcome many inherent weaknesses of traditional HMMs. However, the lack of powerful and efficient statistical learning algorithms is one of the main obstacles preventing them from being well studied and widely used. Since exact inference and learning are intractable, a variational approach is taken to develop effective approximate algorithms. We have implemented the segmental constraint crucial for modeling speech dynamics and present algorithms for recovering hidden speech dynamics and discrete speech units from acoustic data only. The effectiveness of the algorithms developed are verified by experiments on simulation and Switchboard speech data.

Introduces a statistical speech production model. The model synthesizes natural speech by modeling some key dynamic properties of vocal articulators in a linear/nonlinear state-space framework. The goal-oriented movements of the articulators (tongue tip, tongue dorsum, upper lip, lower lip, and jaw) are described in a linear dynamic state equation. The resulting articulatory trajectories, combined with the effects of the velum and larynx, are nonlinearly mapped into the acoustic feature space (MFCCs). The key challenges in this model are the development of a nonlinear parameter estimation methodology, and the incorporation of appropriate prior assumptions to assert in the articulatory dynamic structure. Such a model can also be directly applied to speech recognition to better account for coarticulation and phonetic reduction phenomena with considerably fewer parameters than HMM based approaches.

Transcriptome complexity and its relation to numerous diseases underpins the need to predict in silico splice variants and the regulatory elements that affect them. Building upon our recently described splicing code, we developed AVISPA, a Galaxy-based web tool for splicing prediction and analysis. Given an exon and its proximal sequence, the tool predicts whether the exon is alternatively spliced, displays tissue-dependent splicing patterns, and whether it has associated regulatory elements. We assess AVISPA's accuracy on an independent dataset of tissue-dependent exons, and illustrate how the tool can be applied to analyze a gene of interest. AVISPA is available at http://avispa.biociphers.org.

Transcript quantification is a long-standing problem in genomics and estimating the relative abundance of alternatively-spliced isoforms from the same transcript is an important special case. Both problems have recently been illuminated by high-throughput RNA sequencing experiments which are quickly generating large amounts of data. However, much of the signal present in this data is corrupted or obscured by biases resulting in non-uniform and non-proportional representation of sequences from different transcripts. Many existing analyses attempt to deal with these and other biases with various task-specific approaches, which makes direct comparison between them difficult. However, two popular tools for isoform quantification, MISO and Cufflinks, have adopted a general probabilistic framework to model and mitigate these biases in a more general fashion. These advances motivate the need to investigate the effects of RNA-seq biases on the accuracy of different approaches for isoform quantification. We conduct the investigation by building models of increasing sophistication to account for noise introduced by the biases and compare their accuracy to the established approaches. We focus on methods that estimate the expression of alternatively-spliced isoforms with the percent-spliced-in (PSI) metric for each exon skipping event. To improve their estimates, many methods use evidence from RNA-seq reads that align to exon bodies. However, the methods we propose focus on reads that span only exon-exon junctions. As a result, our approaches are simpler and less sensitive to exon definitions than existing methods, which enables us to distinguish their strengths and weaknesses more easily. We present several probabilistic models of of position-specific read counts with increasing complexity and compare them to each other and to the current state-of-the-art methods in isoform quantification, MISO and Cufflinks. On a validation set with RT-PCR measurements for 26 cassette events, some of our methods are more accurate and some are significantly more consistent than these two popular tools. This comparison demonstrates the challenges in estimating the percent inclusion of alternatively spliced junctions and illuminates the tradeoffs between different approaches.