Trace Reconstruction Problems in Computational Biology.

The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species.

Immunoglobulin genes are formed through V(D)J recombination, which joins the variable (V), diversity (D), and joining (J) germline genes. Since variations in germline genes have been linked to various diseases, personalized immunogenomics focuses on finding alleles of germline genes across various patients. Although reconstruction of V and J genes is a well-studied problem, the more challenging task of reconstructing D genes remained open until the IgScout algorithm was developed in 2019. In this work, we address limitations of IgScout by developing a probabilistic MINING-D algorithm for D gene reconstruction, apply it to hundreds of immunosequencing datasets from multiple species, and validate the newly inferred D genes by analyzing diverse whole genome sequencing datasets and haplotyping heterozygous V genes.

Direct and indirect relationships among posttraumatic stress disorder, depression, hostility, anger, and verbal and physical aggression in returning veterans.

Hostility, anger, and aggression are conceptually related but unique constructs found to occur more often among veterans with posttraumatic stress disorder (PTSD) than among civilians or veterans without PTSD. However, the pathways between PTSD, depression, hostility, anger, and aggression have not been comprehensively characterized. Therefore, drawing on a sample of returning Operation Enduring Freedom/Operation Iraqi Freedom combat veterans ( N = 175; 95% male; mean age 30 years), this study sought to examine the direct and indirect relationships among PTSD, depression, hostility, anger, and four types of aggression: verbal, and physical toward self, others, and objects. Functional modeling of direct effects was done using multiple least-squares regression and bootstrapped mediation analyses were carried out to test indirect effects. Results indicate that PTSD is not the overall direct contributor to different forms of aggression, supporting the mediating role of depression and trait anger. Depression symptoms explain part of the relationships between PTSD and verbal aggression, physical aggression toward objects, and physical aggression toward self and trait anger explains part of the relationships between PTSD and verbal aggression, physical aggression toward objects, and physical aggression toward others. Our findings support the importance of assessing for anger, depression, and different types of aggression among veterans presenting for PTSD treatment to develop individualized treatment plans that may benefit from early incorporation of interventions.