Motivation: Many genomics applications require the computation of nucleotide coverage of a reference genome or the ability to determine how many reads map to a reference region.
Results: BamToCov is a toolkit for rapid and flexible coverage computation that relies on the most memory efficient algorithm and is designed for integration in pipelines, given its ability to read alignment files from streams. The tools in the suite can process sorted BAM or CRAM files, allowing the user to extract coverage information via different filtering approaches and to save the output in different formats (BED, Wig or counts). The BamToCov algorithm can also handle strand-specific and/or physical coverage analyses.
Availability And Implementation: This program, accessory utilities and their documentation are freely available at https://github.com/telatin/BamToCov.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btac125 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Virginia Commonwealth University, Richmond, VA, United States.
Health care is undergoing a "revolution," where patients are becoming consumers and armed with apps, consumer review scores, and, in some countries, high out-of-pocket costs. Although economic analyses and health technology assessment (HTA) have come a long way in their evaluation of the clinical, economic, ethical, legal, and societal perspectives that may be impacted by new technologies and procedures, these approaches do not reflect underlying patient preferences that may be important in the assessment of "value" in the current value-based health care transition. The major challenges that come with the transformation to a value-based health care system lead to questions such as "How are economic analyses, often the basis for policy and reimbursement decisions, going to switch from a societal to an individual perspective?" and "How do we then assess (economic) value, considering individual preference heterogeneity, as well as varying heuristics and decision rules?" These challenges, related to including the individual perspective in cost-effectiveness analysis (CEA), have been widely debated.
View Article and Find Full Text PDFAnnu Rev Public Health
January 2025
2Ross School of Business, University of Michigan, Ann Arbor, Michigan, USA.
A 2008 review in the considered the question of whether health insurance improves health. The answer was a cautious yes because few studies provided convincing causal evidence. We revisit this question by focusing on a single outcome: mortality.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Structural Heart Disease, Cardiovascular Institute and Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Assessing the endothelialization of occlusive devices noninvasively remains a challenge. Cardiac computed tomography angiography (CTA) can be employed to evaluate device endothelialization based on contrast uptake within the occluder.
Objective: This study examined device endothelialization using cardiac CTA and investigated the pathological associations.
PLoS One
January 2025
Department of Political Science, Middlebury College, Middlebury, Vermont, United States of America.
Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment.
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