Biology subjects

DeBlasio, D.

Publications and source records attributed to DeBlasio, D..

5 recordsLinked to original sources

bioRxiv2022

Exploiting Large Datasets Improves Accuracy Estimation for Multiple Sequence Alignment

Multiple sequence alignment plays an important role in many important analyses. However, aligning multiple biological sequences is a complex task, thus many tools have been developed to align sequences under a biologically-inspired objective function. But these tools require a user-defined parameter vector, which if chosen incorrectly, can greatly impact down-stream analysis. Parameter Advising addresses this challenge of selecting input-specific parameter vectors by comparing alignments produced by a carefully constructed set of parameter configurations. In an ideal scenario, we would rank alignments based on their accuracy. However, in practice, we do not have a reference from which to calculate accuracy. Therefore, it becomes necessary to estimate the accuracy to rank the alignments. One solution involves the use of estimators such as Facet. The accuracy estimator Facet computes an estimate of accuracy as a linear combination of efficiently-computable feature functions. In this work we introduce two new estimators called Lead (short for Learned accuracy estimator from large datasets) which use the same underlying feature functions as Facet but are built on top of highly efficient machine learning protocols, allowing us to take advantage of a larger training corpus. Note about previous versionsA previous version of this paper was released on bioRxiv and presented the results of our previous study (Facet) with an error. This error has been corrected, and the conclusions made have been updated based on this new data. This corrected version stands as reference for anyone who may have encountered the versions with inaccuracies.

bioinformatics↗

bioRxiv2019

Practical universal k-mer sets for minimizer schemes

Minimizer schemes have found widespread use in genomic applications as a way to quickly predict the matching probability of large sequences. Most methods for minimizer schemes use randomized (or close to randomized) ordering of k-mers when finding minimizers, but recent work has shown that not all non-lexicographic orderings perform the same. One way to find k-mer orderings for minimizer schemes is through the use of universal k-mer sets, which are subsets of k-mers that are guaranteed to cover all windows. The smaller this set the fewer false positives (where two poorly aligned sequences being identified as possible matches) are identified. Current methods for creating universal k-mer sets are limited in the length of the k-mer that can be considered, and cannot compute sets in the range of lengths currently used in practice. We take some of the first steps in creating universal k-mer sets that can be used to construct minimizer orders for large values of k that are practical. We do this using iterative extension of the k-mers in a set, and guided contraction of the set itself. We also show that this process will be guaranteed to never increase the number of distinct minimizers chosen in a sequence, and thus can only decrease the number of false positives over using the current sets on small k-mers.

bioinformatics↗

bioRxiv2019

Locality sensitive hashing for the edit distance

MotivationSequence alignment is a central operation in bioinformatics pipeline and, despite many improvements, remains a computationally challenging problem. Locality Sensitive Hashing (LSH) is one method used to estimate the likelihood of two sequences to have a proper alignment. Using an LSH, it is possible to separate, with high probability and relatively low computation, the pairs of sequences that do not have an alignment from those that may have an alignment. Therefore, an LSH reduces in the overall computational requirement while not introducing many false negatives (i.e., omitting to report a valid alignment). However, current LSH methods treat sequences as a bag of k-mers and do not take into account the relative ordering of k-mers in sequences. And due to the lack of a practical LSH method for edit distance, in practice, LSH methods for Jaccard similarity or Hamming distance are used as a proxy. ResultsWe present an LSH method, called Order Min Hash (OMH), for the edit distance. This method is a refinement of the minHash LSH used to approximate the Jaccard similarity, in that OMH is not only sensitive to the k-mer contents of the sequences but also to the relative order of the k-mers in the sequences. We present theoretical guarantees of the OMH as a gapped LSH. Contactgmarcais@cs.cmu.edu, carlk@cs.cmu.edu

bioinformatics↗

bioRxiv2018

Automatically eliminating errors induced by suboptimal parameter choices in transcript assembly

Computational tools used for genomic analyses are becoming increasingly sophisticated and complex. While these applications often provide more accurate results than their predecessors, a new problem is emerging in that these pieces of software have a large number of tunable parameters. Choosing the wrong parameter values for an application may lead to significant results being overlooked or false results being reported. We take some first steps towards generating a truly automated genomic analysis pipeline by developing a method for automatically choosing input-specific parameter values for reference-based transcript assembly. We extend the parameter advising framework, first developed for multiple sequence alignment, to optimize parameter choices for the Scallop transcript assembler. In doing so, we provide the first method for finding advisor sets for applications with large numbers of tunable parameters. By choosing parameter values for each input, the area under the curve (AUC) when comparing assembled transcripts to a reference transcriptome is increased by 28.9% over using only the default parameter choices on 1595 RNA-Seq samples in the Sequence Read Archive. This approach is general, and when applied to StringTie it increases AUC by 13.1% on a set of 65 RNA-Seq experiments from ENCODE. Parameter advisors for both Scallop and StringTie are available on Github1.

bioinformatics↗

bioRxiv2018

Asymptotically optimal minimizers schemes

MotivationThe minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density, and thereby making existing and future bioinformatics tools even more efficient.\n\nResultsFrom the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare k-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the 3 type of schemes.\n\nContactgmarcais@cs.cmu.edu ckingsf@cs.cmu.edu

bioinformatics↗