Metagenomic binning with assembly graph embeddings.

MOTIVATION: Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning. RESULTS: We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared with state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning. AVAILABILITY AND IMPLEMENTATION: GraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical and prognostic role of annexin A2 in multiple myeloma.

Annexin A2 (ANXA2) promotes myeloma cell growth, reduces apoptosis in myeloma cell lines, and increases osteoclast formation. ANXA2 has been described in small cohorts of samples as expressed by myeloma cells and cells of the BM microenvironment. To investigate its clinical role, we assessed 1148 samples including independent cohorts of 332 and 701 CD138-purified myeloma cell samples from previously untreated patients together with clinical prognostic factors, chromosomal aberrations, and gene expression-based high-risk scores, along with expression of ANXA2 in whole BM samples, stromal cells, osteoblasts, osteoclasts, and BM sera. ANXA2 is expressed in all normal and malignant plasma cell samples. Higher ANXA2 expression in myeloma cells is associated with significantly inferior event-free and overall survival independently of conventional prognostic factors and is associated with gene expression-determined high risk and high proliferation. Within the BM, all cell populations, including osteoblasts, osteoclasts, and stromal cells, express ANXA2. ANXA2 expression is increased significantly in myelomatous versus normal BM serum. ANXA2 exemplifies an interesting class of targetable bone-remodeling factors expressed by normal and malignant plasma cells and the BM microenvironment that have a significant impact on survival of myeloma patients.

Assigning diagnosis codes using medication history.

Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniques utilizing medical notes but are inapplicable in the absence of these notes. We propose using patients' medication data to assign diagnosis codes. We present a proof-of-concept study using medical data from an American dataset (MIMIC-III) and Danish nationwide registers to train a machine-learning-based model that predicts an extensive collection of diagnosis codes for multiple levels of aggregation over a disease hierarchy. We further suggest a specialized loss function designed to utilize the innate hierarchical nature of the disease hierarchy. We evaluate the proposed method on a subset of 567 disease codes. Moreover, we investigate the technique's generalizability and transferability by (1) training and testing models on the same subsets of disease codes over the two medical datasets and (2) training models on the American dataset while evaluating them on the Danish dataset, respectively. Results demonstrate the proposed method can correctly assign diagnosis codes on multiple levels of aggregation from the disease hierarchy over the American dataset with recall 70.0% and precision 69.48% for top-10 assigned codes; thereby being comparable to text-based techniques. Furthermore, the specialized loss function performs consistently better than the non-hierarchical state-of-the-art version. Moreover, results suggest the proposed method is language and dataset-agnostic, with initial indications of transferability over subsets of disease codes.

A novel sequence variation in the transactivation regulating domain of the human androgen receptor.

OBJECTIVE: To study a novel sequence variation within the androgen receptors' N-terminal CAG repeat region and possible resulting consequences for the receptors' three-dimensional (3D) protein structure. DESIGN: Controlled clinical study. SETTING: University research and andrology clinic. PATIENT(S): Twenty-one adult infertile men. INTERVENTION(S): Ultraviolet laser-assisted microdissection (PALM, Microlaser Technology AG, Bernried, Germany), cloning into pGEM-T vector (Promega, Madison, WI), automated sequencing (Gene Scan 3.7 ABI Prim, Applied Biosystems, Foster City, CA), and Assisted Model Building with Energy Refinement (AMBER). MAIN OUTCOME MEASURE(S): Determination of the sequence of the CAG repeat of the androgen receptor gene and analysis of the 3D protein structure. RESULTS(S): In one hypergonadotropic azoospermic patient with Sertoli-cell-only syndrome, we found a punctual sequence variation of 212A-->G in the CAG repeat resulting in a glutamine-arginine substitution, which leads to a moderate conformational change of the alpha-helix from 34 A in length and 16 A in diameter (without mutation) to a slightly longer helix (43 A) with a smaller diameter (15 A). CONCLUSION(S): Whether the novel 212A-->G exchange in the CAG repeat leading to a glutamine-->arginine substitution and a change in alpha-helix structure may causally be related to the Sertoli-cell-only phenotype of the patient remains to be elucidated.