Biomedical subjects

Uwe Ohler

Publications and source records attributed to Uwe Ohler.

13 recordsLinked to original sources

PubMed2026

Expanding the human proteome with microproteins and peptideins.

A major scientific drive is to characterize the protein-coding genome, which is a primary basis for studying human health. But the fundamental question remains of what has been missed in previous analyses. Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states1-3, with major implications for biomedical science. However, a key gap in knowledge has been which ncORFs produce small microproteins or alternative protein molecules that contribute to the human proteome. Here we report the collaborative efforts of the TransCODE Consortium4 to produce a consensus landscape of protein-level evidence for ncORFs. We show that about 25% of a set of 7,264 ncORFs gives rise to detectable peptides in a large-scale analysis of 95,520 proteomics experiments. We develop an annotation framework for ncORF-encoded microproteins as human proteins and codify the new conceptual model of 'peptideins' as microproteins that have indeterminate potential as functional proteins. To probe the biological implications of peptideins, we create an evolutionary analysis approach, termed ORF relative branch length (ORBL), and determine that evolutionary constraint is common and associates with observation of ncORF-derived peptides. We then characterize a pan-essential cellular phenotype for one peptidein from the OLMALINC long non-coding RNA. Overall, we generate public research tools supported by GENCODE and PeptideAtlas and advance biomedical discovery for understudied components of the human proteome.

Humans↗

PubMed2025

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

GENCODE↗

PubMed2006

Identification of core promoter modules in Drosophila and their application in accurate transcription start site prediction.

The reliable recognition of eukaryotic RNA polymerase II core promoters, and the associated transcription start sites (TSSs) of genes, has been an ongoing challenge for computational biology. High throughput experimental methods such as tiling arrays or 5' SAGE/EST sequencing have recently lead to much larger datasets of core promoters, and to the assessment that the well-known core promoter sequence elements such as the TATA box appear to be much less frequent than thought. Here, we address the co-occurrence of several previously identified core promoter sequence motifs in Drosophila melanogaster to determine frequently occurring core promoter modules. We then use this in a new strategy to model core promoters as a set of alternative submodels for different core promoter architectures reflecting these different motif modules. We show that this system improves greatly on computational promoter recognition and leads to highly accurate in silico TSS prediction. Our results indicate that at least for the case of the fruit fly, we are getting closer to an understanding of how the beginning of a gene is defined in a eukaryotic genome.

Animals↗

PubMed2006

Detection of broadly expressed neuronal genes in C. elegans.

The genes that are expressed in most or all types of neurons define generic neuronal features and provide a window into the developmental origin and function of the nervous system. Few such genes (sometimes referred to as pan-neuronal or broadly expressed neuronal genes) have been defined to date and the mechanisms controlling their regulation are not well understood. As a first step in investigating their regulation, we used a computational approach to detect sequences overrepresented in their promoter elements. We identified a ten-nucleotide cis-regulatory motif shared by many broadly expressed neuronal genes and demonstrated that it is involved in control of neuronal expression. Our results further suggest that global and cell-type-specific controls likely act in concert to establish pan-neuronal gene expression. Using the newly discovered motif and genome-level gene expression data, we identified a set of 234 candidate broadly expressed genes. The known involvement of many of these genes in neurogenesis and physiology of the nervous system supports the utility of this set for future targeted analyses.

Animals↗

PubMed2006

Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment.

BACKGROUND: This study analyzes the predictions of a number of promoter predictors on the ENCODE regions of the human genome as part of the ENCODE Genome Annotation Assessment Project (EGASP). The systems analyzed operate on various principles and we assessed the effectiveness of different conceptual strategies used to correlate produced promoter predictions with the manually annotated 5' gene ends. RESULTS: The predictions were assessed relative to the manual HAVANA annotation of the 5' gene ends. These 5' gene ends were used as the estimated reference transcription start sites. With the maximum allowed distance for predictions of 1,000 nucleotides from the reference transcription start sites, the sensitivity of predictors was in the range 32% to 56%, while the positive predictive value was in the range 79% to 93%. The average distance mismatch of predictions from the reference transcription start sites was in the range 259 to 305 nucleotides. At the same time, using transcription start site estimates from DBTSS and H-Invitational databases as promoter predictions, we obtained a sensitivity of 58%, a positive predictive value of 92%, and an average distance from the annotated transcription start sites of 117 nucleotides. In this experiment, the best performing promoter predictors were those that combined promoter prediction with gene prediction. The main reason for this is the reduced promoter search space that resulted in smaller numbers of false positive predictions. CONCLUSION: The main finding, now supported by comprehensive data, is that the accuracy of human promoter predictors for high-throughput annotation purposes can be significantly improved if promoter prediction is combined with gene prediction. Based on the lessons learned in this experiment, we propose a framework for the preparation of the next similar promoter prediction assessment.

Computational Biology↗

PubMed2006

Quantification of transcription factor expression from Arabidopsis images.

MOTIVATION: Confocal microscopy has long provided qualitative information for a variety of applications in molecular biology. Recent advances have led to extensive image datasets, which can now serve as new data sources to obtain quantitative gene expression information. In contrast to microarrays, which usually provide data for many genes at one time point, these image data provide us with expression information for only one gene, but with the advantage of high spatial and/or temporal resolution, which is often lostin microarray samples. RESULTS: We have developed a prototype for the automatic analysis of Arabidopsis confocal images, which show the expression of a single transcription factor by means of GFP reporter constructs. Using techniques from image registration, we are able to address inherent problems of non-rigid transformation and partial mapping, and obtain relative expression values for 13 different tissues in Arabidopsis roots. This provides quantitative information with high spatial resolution, which accurately represents the underlying expression values within the organism. We validate our approach on a data set of 122 images depicting expression patterns of 30 transcription factors, both in terms of registration accuracy, as well as correlation with cell-sorted microarray data. Approaches like this will be useful to lay the groundwork to reconstruct regulatory networks on the level of tissues or even individual cells. AVAILABILITY: Upon request from the authors.

Arabidopsis Proteins↗

PubMed2006

Informative priors based on transcription factor structural class improve de novo motif discovery.

MOTIVATION: An important problem in molecular biology is to identify the locations at which a transcription factor (TF) binds to DNA, given a set of DNA sequences believed to be bound by that TF. In previous work, we showed that information in the DNA sequence of a binding site is sufficient to predict the structural class of the TF that binds it. In particular, this suggests that we can predict which locations in any DNA sequence are more likely to be bound by certain classes of TFs than others. Here, we argue that traditional methods for de novo motif finding can be significantly improved by adopting an informative prior probability that a TF binding site occurs at each sequence location. To demonstrate the utility of such an approach, we present priority, a powerful new de novo motif finding algorithm. RESULTS: Using data from TRANSFAC, we train three classifiers to recognize binding sites of basic leucine zipper, forkhead, and basic helix loop helix TFs. These classifiers are used to equip priority with three class-specific priors, in addition to a default prior to handle TFs of other classes. We apply priority and a number of popular motif finding programs to sets of yeast intergenic regions that are reported by ChIP-chip to be bound by particular TFs. priority identifies motifs the other methods fail to identify, and correctly predicts the structural class of the TF recognizing the identified binding sites. AVAILABILITY: Supplementary material and code can be found at http://www.cs.duke.edu/~amink/.

Algorithms↗

PubMed2006

Optimized mixed Markov models for motif identification.

BACKGROUND: Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. RESULTS: We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at 1. CONCLUSION: Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods.

Algorithms↗

PubMed2006

Transcriptional and posttranscriptional regulation of transcription factor expression in Arabidopsis roots.

Understanding how the expression of transcription factor (TF) genes is modulated is essential for reconstructing gene regulatory networks. There is increasing evidence that sequences other than upstream noncoding can contribute to modulating gene expression, but how frequently they do so remains unclear. Here, we investigated the regulation of TFs expressed in a tissue-enriched manner in Arabidopsis roots. For 61 TFs, we created GFP reporter constructs driven by each TF's upstream noncoding sequence (including the 5'UTR) fused to the GFP reporter gene alone or together with the TF's coding sequence. We compared the visually detectable GFP patterns with endogenous mRNA expression patterns, as defined by a genome-wide microarray root expression map. An automated image analysis method for quantifying GFP signals in different tissues was developed and used to validate our visual comparison method. From these combined analyses, we found that (i) the upstream noncoding sequence was sufficient to recapitulate the mRNA expression pattern for 80% (35/44) of the TFs, and (ii) 25% of the TFs undergo posttranscriptional regulation via microRNA-mediated mRNA degradation (2/24) or via intercellular protein movement (6/24). The results suggest that, for Arabidopsis TFs, upstream noncoding sequences are major contributors to mRNA expression pattern establishment, but modulation of transcription factor protein expression pattern after transcription is relatively frequent. This study provides a systematic overview of regulation of TF expression at a cellular level.

Arabidopsis↗

PubMed2005

Recognition of unknown conserved alternatively spliced exons.

The split structure of most mammalian protein-coding genes allows for the potential to produce multiple different mRNA and protein isoforms from a single gene locus through the process of alternative splicing (AS). We propose a computational approach called UNCOVER based on a pair hidden Markov model to discover conserved coding exonic sequences subject to AS that have so far gone undetected. Applying UNCOVER to orthologous introns of known human and mouse genes predicts skipped exons or retained introns present in both species, while discriminating them from conserved noncoding sequences. The accuracy of the model is evaluated on a curated set of genes with known conserved AS events. The prediction of skipped exons in the approximately 1% of the human genome represented by the ENCODE regions leads to more than 50 new exon candidates. Five novel predicted AS exons were validated by RT-PCR and sequencing analysis of 15 introns with strong UNCOVER predictions and lacking EST evidence. These results imply that a considerable number of conserved exonic sequences and associated isoforms are still completely missing from the current annotation of known genes. UNCOVER also identifies a small number of candidates for conserved intron retention.

Journal Article↗

PubMed2004

The MTE, a new core promoter element for transcription by RNA polymerase II.

The core promoter is the ultimate target of the vast network of regulatory factors that contribute to the initiation of transcription by RNA polymerase II. Here we describe the MTE (motif ten element), a new core promoter element that appears to be conserved from Drosophila to humans. The MTE promotes transcription by RNA polymerase II when it is located precisely at positions +18 to +27 relative to A(+1) in the initiator (Inr) element. MTE sequences from +18 to +22 relative to A(+1) are important for basal transcription, and a region from +18 to +27 is sufficient to confer MTE activity to heterologous core promoters. The MTE requires the Inr, but functions independently of the TATA-box and DPE. Notably, the loss of transcriptional activity upon mutation of a TATA-box or DPE can be compensated by the addition of an MTE. In addition, the MTE exhibits strong synergism with the TATA-box as well as the DPE. These findings indicate that the MTE is a novel downstream core promoter element that is important for transcription by RNA polymerase II.

Base Sequence↗

PubMed2004

Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification.

MicroRNAs are approximately 22-nucleotide (nt) RNAs processed from foldback segments of endogenous transcripts. Some are known to play important gene regulatory roles during animal and plant development by pairing to the messages of protein-coding genes to direct the post-transcriptional repression of these messages. Previously, we developed a computational method called MiRscan, which scores features related to the foldbacks, and used this algorithm to identify new miRNA genes in the nematode Caenorhabditis elegans. In the present study, to identify sequences that might be involved in processing or transcriptional regulation of miRNAs, we aligned sequences upstream and downstream of orthologous nematode miRNA foldbacks. These alignments showed a pronounced peak in sequence conservation about 200 bp upstream of the miRNA foldback and revealed a highly significant sequence motif, with consensus CTCCGCCC, that is present upstream of almost all independently transcribed nematode miRNA genes. Scoring the pattern of upstream/downstream conservation, the occurrence of this sequence motif, and orthology of host genes for intronic miRNA candidates, yielded substantial improvements in the accuracy of MiRscan. Nine new C. elegans miRNA gene candidates were validated using a PCR-sequencing protocol. As previously seen for bacterial RNA genes, sequence features outside of the RNA secondary structure can therefore be very useful for the computational identification of eukaryotic noncoding RNA genes. The total number of confidently identified nematode miRNAs now approaches 100. The improved analysis supports our previous assertion that miRNA gene identification is nearing completion in C. elegans with apparently no more than 20 miRNA genes now remaining to be identified.

Animals↗

PubMed2002

Computational analysis of core promoters in the Drosophila genome.

BACKGROUND: The core promoter, a region of about 100 base-pairs flanking the transcription start site (TSS), serves as the recognition site for the basal transcription apparatus. Drosophila TSSs have generally been mapped by individual experiments; the low number of accurately mapped TSSs has limited analysis of promoter sequence motifs and the training of computational prediction tools. RESULTS: We identified TSS candidates for about 2,000 Drosophila genes by aligning 5' expressed sequence tags (ESTs) from cap-trapped cDNA libraries to the genome, while applying stringent criteria concerning coverage and 5'-end distribution. Examination of the sequences flanking these TSSs revealed the presence of well-known core promoter motifs such as the TATA box, the initiator and the downstream promoter element (DPE). We also define, and assess the distribution of, several new motifs prevalent in core promoters, including what appears to be a variant DPE motif. Among the prevalent motifs is the DNA-replication-related element DRE, recently shown to be part of the recognition site for the TBP-related factor TRF2. Our TSS set was then used to retrain the computational promoter predictor McPromoter, allowing us to improve the recognition performance to over 50% sensitivity and 40% specificity. We compare these computational results to promoter prediction in vertebrates. CONCLUSIONS: There are relatively few recognizable binding sites for previously known general transcription factors in Drosophila core promoters. However, we identified several new motifs enriched in promoter regions. We were also able to significantly improve the performance of computational TSS prediction in Drosophila.

Animals↗