Biology subjects

Padmanabhan, R.

Publications and source records attributed to Padmanabhan, R..

4 recordsLinked to original sources

bioRxiv2020

Predicting primer and panel off-target rate in QIAseq targeted DNA panels using convolutional neural networks

In QIAseq targeted DNA panels, synthetic primers (short single-strand DNA sequences) are used for target enrichment via complementary DNA binding. Off-target priming could occur in this process when a primer binds to some loci where the DNA sequences are identical or very similar to the target template. These off-target DNA segments go through the rest of the workflow, wasting sequencing resources in unwanted regions. Off-target cannot be avoided if some segments of the target region are repetitive throughout the genome, nor can it be quantified until after sequencing. But if off-target rates can be prospectively predicted, scientists can make informed decisions about investment on high off-target panels. We developed pordle (predicting off-target rate with deep learning and epcr07), a convolutional neural network (CNN) model to predict off-target binding events of a given primer. The neural network was trained using 10 QIAseq DNA panels with 29,274 unique primers and then tested on an independent QIAseq panel with 7,576 primers. The model predicted a 10.5% off-target rate for the test panel, a -0.1% bias from the true value of 10.6%. The model successfully selected the better primer (in terms of off-target rate) for 89.2% of 3,835 pairs of close-by primers in the test panel whose off-target rates differ by at least 10%. The order-preserving property may help panel developers select the optimal primer from a group of candidates, which is a common task in panel design.

bioinformatics↗

bioRxiv2019

The Length Of Promoter Sequence Affects The De Novo Initiation By T7 RNA Polymerase In Vitro: New Insights Into The Evolution Of Promoters For Single Subunit RNA Polymerases.

1.1RNA polymerases (RNAPs) differ from other polymerases in that they can bind promoter sequences and initiate de novo transcription. Promoter recognition requires the presence of specific DNA binding domains in the polymerase. The structure and mechanistic aspects of transcription by the bacteriophage T7 RNA polymerase (T7 RNAP) are well characterized. This single subunit RNAP belongs to the family of RNAPs which also includes the T3, SP6 and mitochondrial RNAPs. High specificity for its promoter, the requirement of no additional transcription factors, and high fidelity of initiation from a specific site in the promoter makes it the polymerase of choice to study the mechanistic aspects of transcription. The structure and function of the catalytic domains of this family of polymerases are highly conserved suggesting a common mechanism underlying transcription. Although the two groups of single subunit RNAPs, mitochondrial and bacteriophage, have remarkable structural conservation, they recognize quite dissimilar promoters. Specifically, the bacteriophage promoters recognize a 23 nucleotide promoter extending from -17 to + 6 nucleotides relative to the site of transcription initiation, while the well characterized promoter recognized by the yeast mitochondrial RNAP is nine nucleotides in length extending from -8 to +1 relative to the site of transcription initiation. Promoters recognized by the bacteriophage RNAPs are also well characterized with distinct functional domains involved in promoter recognition and transcription initiation. Thorough mutational studies have been conducted by altering individual base-pairs within these domains. Here we describe experiments to determine whether the prototype bacteriophage RNAP is able to recognize and initiate at truncated promoters similar to mitochondrial promoters. Using an in vitro oligonucleotide transcriptional system, we have assayed transcription initiation activity by T7 RNAP. When a complete or almost complete (20 to 16 nucleotide) double stranded T7 RNAP promoter sequence is present, small RNAs are produced through template-independent and promoter-dependent stuttering corresponding to abortive initiation, and this effect was lost with a scrambled promoter sequence. When partial double stranded promoter sequences (10 to 12 nucleotides) are supplied, template dependent de novo initiation of RNA occurs at a site different from the canonical +1-initiation site. The site of transcription initiation is determined by a recessed 3 end based paired to the template strand of DNA rather than relative to the partial promoter sequence. Understanding the mechanism underlying this observation helps us to understand the role of the elements in the T7 promoter, and provides insights into the promoter evolution of the single-subunit RNAPs.

molecular biology↗

bioRxiv2018

smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers

MotivationLow-frequency DNA mutations are often confounded with technical artifacts from sample preparation and sequencing. With unique molecular identifiers (UMIs), most of the sequencing errors can be corrected. However, errors before UMI tagging, such as DNA polymerase errors during end-repair and the first PCR cycle, cannot be corrected with single-strand UMIs and impose fundamental limits to UMI-based variant calling.\n\nResultsWe developed smCounter2, a UMI-based variant caller for targeted sequencing data and an upgrade from the current version of smCounter. Compared to smCounter, smCounter2 features lower detection limit at 0.5%, better overall accuracy (particularly in non-coding regions), a consistent threshold that can be applied to both deep and shallow sequencing runs, and easier use via a Docker image and code for read pre-processing. We benchmarked smCounter2 against several state-of-the-art UMI-based variant calling methods using multiple datasets and demonstrated smCounter2s superior performance in detecting somatic variants. At the core of smCounter2 is a statistical test to determine whether the allele frequency of the putative variant is significantly above the background error rate, which was carefully modeled using an independent dataset. The improved accuracy in non-coding regions was mainly achieved using novel repetitive region filters that were specifically designed for UMI data.\n\nAvailabilityThe entire pipeline is available at https://github.com/qiaseq/qiaseq-dna under MIT license.

bioinformatics↗

bioRxiv2017

Modeling of a negative feedback mechanism explains age-dependent genetic architecture in reproduction in domesticated C. elegans strains

Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute, but can depend on a number of factors including the age and genetic background of an organism. In order to understand the mechanisms that cause these changes, we are studying heritable trait differences between two domesticated strains of C. elegans. We previously identified a major effect locus, caused by a mutation in a component of the NURF chromatin remodeling complex, that regulated reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animals reproductive lifespan. Using a previously published macroscale model of egg-laying rate in C. elegans, we show how time-dependent effect-size can be explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate a number of key predictions of this model using controlled mating experiments and quantification of oogenesis and sperm use. By incorporating this model into QTL mapping, we identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.\n\nAUTHOR SUMMARYComplex traits are influenced not only by the individual effects of genetic variants, but also how these variants interact with the environment, age, and each other. While complex genetic architectures seem to be ubiquitous in natural traits, little is known about the mechanisms that cause them. Here we identify an example of age-dependent genetic architecture controlling the rate and timing of reproduction in the hermaphroditic nematode C. elegans. Using computational modeling, we demonstrate how this age-dependent genetic architecture can arise as a consequence of two factors: hormonal feedback on oocytes mediated by major sperm protein (MSP) released by sperm stored in the spermatheca and life history differences in sperm use caused by genetic variants. Our work also suggests how age-dependent epistasis can emerge from multicellular feedback systems.

genetics↗