DeGeCI 1.1: a web platform for gene annotation of mitochondrial genomes.

Summary: DeGeCI is a command line tool that generates fully automated de novo gene predictions from mitochondrial nucleotide sequences by using a reference database of annotated mitogenomes which is represented as a de Bruijn graph. The input genome is mapped to this graph, creating a subgraph, which is then post-processed by a clustering routine. Version 1.1 of DeGeCI offers a web front-end for GUI-based input. It also introduces a new taxonomic filter pipeline that allows the species in the reference database to be restricted to a user-specified taxonomic classification and allows for gene boundary optimization when providing the translation table of the input genome. Availability and implementation: The web platform is accessible at https://degeci.informatik.uni-leipzig.de. Source code is freely available at https://git.informatik.uni-leipzig.de/lfiedler/degeci.

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations.

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how human-computer interaction (HCI) and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 97 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, usability, and human-AI collaboration performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.

Fully automated annotation of mitochondrial genomes using a cluster-based approach with de Bruijn graphs.

A wide range of scientific fields, such as forensics, anthropology, medicine, and molecular evolution, benefits from the analysis of mitogenomic data. With the development of new sequencing technologies, the amount of mitochondrial sequence data to be analyzed has increased exponentially over the last few years. The accurate annotation of mitochondrial DNA is a prerequisite for any mitogenomic comparative analysis. To sustain with the growth of the available mitochondrial sequence data, highly efficient automatic computational methods are, hence, needed. Automatic annotation methods are typically based on databases that contain information about already annotated (and often pre-curated) mitogenomes of different species. However, the existing approaches have several shortcomings: 1) they do not scale well with the size of the database; 2) they do not allow for a fast (and easy) update of the database; and 3) they can only be applied to a relatively small taxonomic subset of all species. Here, we present a novel approach that does not have any of these aforementioned shortcomings, (1), (2), and (3). The reference database of mitogenomes is represented as a richly annotated de Bruijn graph. To generate gene predictions for a new user-supplied mitogenome, the method utilizes a clustering routine that uses the mapping information of the provided sequence to this graph. The method is implemented in a software package called DeGeCI (De Bruijn graph Gene Cluster Identification). For a large set of mitogenomes, for which expert-curated annotations are available, DeGeCI generates gene predictions of high conformity. In a comparative evaluation with MITOS2, a state-of-the-art annotation tool for mitochondrial genomes, DeGeCI shows better database scalability while still matching MITOS2 in terms of result quality and providing a fully automated means to update the underlying database. Moreover, unlike MITOS2, DeGeCI can be run in parallel on several processors to make use of modern multi-processor systems.

Detecting gene breakpoints in noisy genome sequences using position-annotated colored de-Bruijn graphs.

BACKGROUND: Identifying the locations of gene breakpoints between species of different taxonomic groups can provide useful insights into the underlying evolutionary processes. Given the exact locations of their genes, the breakpoints can be computed without much effort. However, often, existing gene annotations are erroneous, or only nucleotide sequences are available. Especially in mitochondrial genomes, high variations in gene orders are usually accompanied by a high degree of sequence inconsistencies. This makes accurately locating breakpoints in mitogenomic nucleotide sequences a challenging task. RESULTS: This contribution presents a novel method for detecting gene breakpoints in the nucleotide sequences of complete mitochondrial genomes, taking into account possible high substitution rates. The method is implemented in the software package DeBBI. DeBBI allows to analyze transposition- and inversion-based breakpoints independently and uses a parallel program design, allowing to make use of modern multi-processor systems. Extensive tests on synthetic data sets, covering a broad range of sequence dissimilarities and different numbers of introduced breakpoints, demonstrate DeBBI 's ability to produce accurate results. Case studies using species of various taxonomic groups further show DeBBI 's applicability to real-life data. While (some) multiple sequence alignment tools can also be used for the task at hand, we demonstrate that especially gene breaks between short, poorly conserved tRNA genes can be detected more frequently with the proposed approach. CONCLUSION: The proposed method constructs a position-annotated de-Bruijn graph of the input sequences. Using a heuristic algorithm, this graph is searched for particular structures, called bulges, which may be associated with the breakpoint locations. Despite the large size of these structures, the algorithm only requires a small number of graph traversal steps.

Diffusing innovative road safety practice: A social network approach to identifying opinion leading U.S. cities.

OBJECTIVE: This study sought to identify opinion-leading U.S. cities in the realm of safe transportation systems by surveying road safety professionals and asking them to identify places that served as models for road safety. METHODS: Using a purposive sampling methodology, we surveyed professionals employed in road safety-related professions (e.g., transportation engineering, planning, public health, law enforcement, and emergency response). Using 183 professionals' complete responses, we carried out social network analysis to both describe the structure of intermunicipal advice-seeking patterns among road safety professionals and identify those municipalities with relatively high degrees of influence. RESULTS: We discovered a large intermunicipal monitoring network related to improving road user safety. Half of the network ties (50.4%) crossed regional U.S. census boundaries. Social network statistics informed the identification of 7 opinion-leader and 4 boundary-spanning municipalities. CONCLUSIONS: This study indicated a large intermunicipal monitoring network, half of which crossed regional boundaries. Road safety professionals have formed a country-spanning example-following network on the topic of improving road user safety in the United States. Researchers and intervention teams can tap into this network to accelerate the uptake and spread of evidence-based road safety practices.

PLAC8 Localizes to the Inner Plasma Membrane of Pancreatic Cancer Cells and Regulates Cell Growth and Disease Progression through Critical Cell-Cycle Regulatory Pathways.

Pancreatic ductal adenocarcinoma (PDAC) carries the most dismal prognosis of all solid tumors and is generally strongly resistant to currently available chemo- and/or radiotherapy regimens, including targeted molecular therapies. Therefore, unraveling the molecular mechanisms underlying the aggressive behavior of pancreatic cancer is a necessary prerequisite for the development of novel therapeutic approaches. We previously identified the protein placenta-specific 8 (PLAC8, onzin) in a genome-wide search for target genes associated with pancreatic tumor progression and demonstrated that PLAC8 is strongly ectopically expressed in advanced preneoplastic lesions and invasive human PDAC. However, the molecular function of PLAC8 remained unclear, and accumulating evidence suggested its role is highly dependent on cellular and physiologic context. Here, we demonstrate that in contrast to other cellular systems, PLAC8 protein localizes to the inner face of the plasma membrane in pancreatic cancer cells, where it interacts with specific membranous structures in a temporally and spatially stable manner. Inhibition of PLAC8 expression strongly inhibited pancreatic cancer cell growth by attenuating cell-cycle progression, which was associated with transcriptional and/or posttranslational modification of the central cell-cycle regulators CDKN1A, retinoblastoma protein, and cyclin D1 (CCND1), but did not impact autophagy. Moreover, Plac8 deficiency significantly inhibited tumor formation in genetically engineered mouse models of pancreatic cancer. Together, our findings establish PLAC8 as a central mediator of tumor progression in PDAC and as a promising candidate gene for diagnostic and therapeutic targeting.

Differentiation of multiple types of pancreatico-biliary tumors by molecular analysis of clinical specimens.

Timely and accurate diagnosis of pancreatic ductal adenocarcinoma (PDAC) is critical in order to provide adequate treatment to patients. However, the clinical signs and symptoms of PDAC are shared by several types of malignant or benign tumors which may be difficult to differentiate from PDAC with conventional diagnostic procedures. Among others, these include ampullary cancers, solid pseudopapillary tumors, and adenocarcinomas of the distant bile duct, as well as inflammatory masses developing in chronic pancreatitis. Here, we report an approach to accurately differentiate between these different types of pancreatic masses based on molecular analysis of biopsy material. A total of 156 bulk tissue and fine needle aspiration biopsy samples were analyzed using a dedicated diagnostic cDNA array and a composite classification algorithm developed based on linear support vector machines. All five histological subtypes of pancreatic masses were clearly separable with 100% accuracy when using all 156 individual samples for classification. Generalized performance of the classification system was tested by 10 × 10-fold cross validation (100 test runs). Correct classification into the five diagnostic groups was demonstrated for 81.5% of 1,560 test set predictions. Performance increased to 85.3% accuracy when PDAC and distant bile duct carcinomas were combined in a single diagnostic class. Importantly, overall sensitivity of detection of malignant disease was 92.2%. The molecular diagnostic approach presented here is suitable to significantly aid in the differential diagnosis of undetermined pancreatic masses. To our knowledge, this is the first study reporting accurate differentiation between several types of pancreatico-biliary tumors in a single molecular analytical procedure.