Biomedical subjects

Brian Tjaden

Publications and source records attributed to Brian Tjaden.

5 recordsLinked to original sources

PubMed2025

Popcorn: prediction of short coding and noncoding genomic sequences in prokaryotes.

SUMMARY: The most challenging prokaryotic genes to identify often correspond to short ORFs (sORFs) encoding small proteins or to noncoding RNAs. RNA-seq experiments commonly evince small transcripts that do not correspond to annotated genes and are candidates for novel coding sORFs or small regulatory RNAs, but it can be difficult to accurately assess whether the numerous small transcripts are coding or not. We present Popcorn (PrOkaryotic Prediction of Coding OR Noncoding), a novel machine learning method for determining whether prokaryotic sequences are coding or noncoding. We find that Popcorn is effective in distinguishing coding from noncoding sequences, including coding sORFs and noncoding RNAs. AVAILABILITY AND IMPLEMENTATION: Freely available for use on the web at https://cs.wellesley.edu/∼btjaden/Popcorn. Source code available at https://github.com/btjaden/Popcorn and https://doi.org/10.5281/zenodo.15120075.

Open Reading Frames↗

PubMed2006

Target prediction for small, noncoding RNAs in bacteria.

Many small, noncoding RNAs in bacteria act as post-transcriptional regulators by basepairing with target mRNAs. While the number of characterized small RNAs (sRNAs) has steadily increased, only a limited number of the corresponding mRNA targets have been identified. Here we present a program, TargetRNA, that predicts the targets of these bacterial RNA regulators. The program was evaluated by assessing whether previously known targets could be identified. The program was then used to predict targets for the Escherichia coli RNAs RyhB, OmrA, OmrB and OxyS, and the predictions were compared with changes in whole genome expression patterns observed upon expression of the sRNAs. Our results show that TargetRNA is a useful tool for finding mRNA targets of sRNAs, although its rate of success varies between sRNAs.

Base Pairing↗

PubMed2006

An approach for clustering gene expression data with error information.

BACKGROUND: Clustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated genes. Even the best clustering methods, however, are unlikely to provide meaningful results if too much of the data is unreliable. With the maturation of microarray technology, a wealth of research on statistical analysis of gene expression data has encouraged researchers to consider error and uncertainty in their microarray experiments, so that experiments are being performed increasingly with repeat spots per gene per chip and with repeat experiments. One of the challenges is to incorporate the measurement error information into downstream analyses of gene expression data, such as traditional clustering techniques. RESULTS: In this study, a clustering approach is presented which incorporates both gene expression values and error information about the expression measurements. Using repeat expression measurements, the error of each gene expression measurement in each experiment condition is estimated, and this measurement error information is incorporated directly into the clustering algorithm. The algorithm, CORE (Clustering Of Repeat Expression data), is presented and its performance is validated using statistical measures. By using error information about gene expression measurements, the clustering approach is less sensitive to noise in the underlying data and it is able to achieve more accurate clusterings. Results are described for both synthetic expression data as well as real gene expression data from Escherichia coli and Saccharomyces cerevisiae. CONCLUSION: The additional information provided by replicate gene expression measurements is a valuable asset in effective clustering. Gene expression profiles with high errors, as determined from repeat measurements, may be unreliable and may associate with different clusters, whereas gene expression profiles with low errors can be clustered with higher specificity. Results indicate that including error information from repeat gene expression measurements can lead to significant improvements in clustering accuracy.

Algorithms↗

PubMed2002

Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays.

Microarrays traditionally have been used to analyze the expression behavior of large numbers of coding transcripts. Here we present a comprehensive approach for high-throughput transcript discovery in Escherichia coli focused mainly on intergenic regions which, together with analysis of coding transcripts, provides us with a more complete insight into the organism's transcriptome. Using a whole genome array, we detected expression for 4052 coding transcripts and identified 1102 additional transcripts in the intergenic regions of the E.coli genome. Further classification reveals 317 novel transcripts with unknown function. Our results show that, despite sophisticated approaches to genome annotation, many cellular transcripts remain unidentified. Through the experimental identification of all RNAs expressed under a specific condition, we gain a more thorough understanding of all cellular processes.

3' Untranslated Regions↗

PubMed2002

Identifying operons and untranslated regions of transcripts using Escherichia coli RNA expression analysis.

Microarrays traditionally have been used to assay the transcript expression of coding regions of genes. Here, we use Escherichia coli oligonucleotide microarrays to assay transcript expression of both open reading frames (ORFs) and intergenic regions. We then use hidden Markov models to analyse this expression data and estimate transcription boundaries of genes. This approach allows us to identify 5' untranslated regions (5' UTRs) of transcripts as well as genes that are likely to be operon members. The operon elements we identify correspond to documented operons with 99% specificity and 63% sensitivity. Similarly we find that our 5' UTR results accurately coincide with experimentally verified promoter regions for most genes.

5' Untranslated Regions↗