This chapter describes procedures for the use of DNA sequence data to obtain and compare taxonomic identification using the public databases GenBank and Barcode of Life Data System (BOLD). The chapter begins by describing procedures used to prepare quality sequences for uploading into GenBank and BOLD. Next, steps used to query the DNA sequences against the public databases are described using GenBank BLAST and BOLD identification engines. Interpretation guidelines for the taxonomic identification assignments are presented. Finally, a procedure for evaluating the accuracy and reliability of sequences from GenBank and BOLD is provided.

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

Standardized nomenclature for genes, gene products, and isoforms is crucial to prevent ambiguity and enable clear communication of scientific data, facilitating efficient biocuration and data sharing. Standardized genotype nomenclature, which describes alleles present in a specific strain that differ from those in the wild-type reference strain, is equally essential to maximize research impact and ensure that results linking genotypes to phenotypes are Findable, Accessible, Interoperable, and Reusable (FAIR). In this publication, we extend the fission yeast clade gene nomenclature guidelines to support the curation efforts at PomBase (www.pombase.org), the Schizosaccharomyces pombe Model Organism Database. This update introduces nomenclature guidelines for noncoding RNA genes, following those set forth by the Human Genome Organisation Gene Nomenclature Committee. Additionally, we provide a significant update to the allele and genotype nomenclature guidelines originally published in 1987, to standardize the diverse range of genetic modifications enabled by the fission yeast genetic toolbox. These updated guidelines reflect a community consensus between numerous fission yeast researchers. Adoption of these rules will improve consistency in gene and genotype nomenclature, and facilitate machine-readability and automated entity recognition of fission yeast genes and alleles in publications or datasets. In conclusion, our updated guidelines provide a valuable resource for the fission yeast research community, promoting consistency, clarity, and FAIRness in genetic data sharing and interpretation.

B cell acute lymphoblastic leukemia (B-ALL) is the most prevalent pediatric cancer. MicroRNAs (miRNAs) are 18-22nt non-coding transcripts shown to be essential for the development of many cancers. While some miRNAs are reportedly expressed differentially between healthy and B-ALL, no studies have reported a consensus miRNA signature. Therefore, we performed a reanalysis of five miRNA datasets to identify differentially expressed miRNAs (DEmiRs) and a meta-analysis of previously identified DEmiRs from 25 studies. Overall, the re-analysis showed that the DEmiR data clustered by platform and not by disease state. The meta-analysis also did not reveal a consensus miRNA signature as there were many miRNAs upregulated in some studies and downregulated in others. However, eight promising miRNAs (miR-181b, miR-128b, miR-181a, miR-128, miR-128a, miR-181c, miR-155, miR-142-3p, and miR-451) were identified from the meta-analysis, which could be the basis of future investigations. These analyses reveal that standardization of miRNA isolation and analysis is needed in B-ALL to enable cross-study comparisons and identify a consensus signature.

High-precision human leukocyte antigen (HLA) genotyping is crucial for anti-cancer immunotherapy, but existing tools predicting HLA genotypes using next-generation sequencing (NGS) data are insufficiently accurate.
We compared availability, accuracy, correction score, and complementary ratio of eight HLA genotyping tools (OptiType, HLA-HD, PHLAT, seq2HLA, arcasHLA, HLAscan, HLA*LA, and Kourami) using 1,005 cases from the 1000 Genomes Project data. We created a new HLA-genotyping algorithm combining tools based on the precision and the accuracy of tools\' combinations. Then, we assessed the new algorithm\'s performance in 39 in-house samples with normal whole-exome sequencing (WES) data and polymerase chain reaction-sequencing-based typing (PCR-SBT) results.
Regardless of the type of tool, the calls presented by more than six tools concordantly showed high accuracy and precision. The accuracy of the group with at least six concordant calls was 100% (97/97) in HLA-A, 98.2% (112/114) in HLA-B, 97.3% (142/146) in HLA-C. The precision of the group with at least six concordant calls was over 98% in HLA-ABC. We additionally calculated the accuracy of the combination tools considering the complementary ratio of each tool and the accuracy of each tool, and the accuracy was over 98% in all groups with six or more concordant calls. We created a new algorithm that matches the above results. It was to select the HLA type if more than six out of eight tools presented a matched type. Otherwise, determine the HLA type experimentally through PCR-SBT. When we applied the new algorithm to 39 in-house cases, there were more than six matching calls in all HLA-A, B, and C, and the accuracy of these concordant calls was 100%.
HLA genotyping accuracy using NGS data could be increased by combining the current HLA genotyping tools. This new algorithm could also be useful for preliminary screening to decide whether to perform an additional PCR-based experimental method instead of using tools with NGS data.

The 2018 American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guideline on HER2 testing in breast cancer permits reclassification of cases with HER2-equivocal results by FISH. The impact of such reclassification is unclear. We sought to determine the proportion of HER2-equivocal cases that are reclassified as HER2-negative and the impact of anti-HER2 therapy on survival in HER2-equivocal cases.
We reviewed medical records of breast cancer patients who had HER2 testing by fluorescence in stitu hybridization (FISH) and immunohistochemistry (IHC) performed or verified at The University of Texas MD Anderson Cancer Center during April 2014 through March 2018 and had equivocal results according to the 2013 ASCO/CAP guideline. The population was divided into 2 cohorts according to whether the biopsy specimen analyzed came from primary or from recurrent or metastatic disease. HER2 status was reclassified according to the 2018 ASCO/CAP guideline. Overall survival (OS) and event-free survival (EFS) were calculated using the Kaplan-Meier method, and the relationship between anti-HER2 therapy and clinical outcomes was assessed.
We identified 139 cases with HER2-equivocal results according to the 2013 ASCO/CAP guideline: 90 cases of primary disease and 49 cases of recurrent/metastatic disease. Per the 2018 ASCO/CAP guideline, these cases were classified as follows: overall, HER2-negative 112 cases (80%), HER2-positive 1 (1%), and unknown 26 (19%); primary cohort, HER2-negative 85 (94%), HER2-positive 1 (1%), unknown 4 (4%); and recurrent/metastatic, HER2-negative 27 (55%) and unknown 22 (45%). Five patients in the primary-disease cohort and 1 patient in the recurrent/metastatic-disease cohort received anti-HER2 therapy. There was no significant association between anti-HER2 therapy and OS or EFS in either cohort (primary disease: OS, p = 0.67; EFS, p = 0.49; recurrent/metastatic-disease, OS, p = 0.61; EFS, p = 0.78.
The majority of HER2-equivocal breast cancer cases were reclassified as HER2-negative per the 2018 ASCO/CAP guideline. No association between anti-HER2 therapy and OS or EFS was observed. HER2-equivocal cases seem to have clinical behavior similar to that of HER2-negative breast cancers.

Mitochondrial DNA (mtDNA) variant pathogenicity interpretation has special considerations given unique features of the mtDNA genome, including maternal inheritance, variant heteroplasmy, threshold effect, absence of splicing, and contextual effects of haplogroups. Currently, there are insufficient standardized criteria for mtDNA variant assessment, which leads to inconsistencies in clinical variant pathogenicity reporting. An international working group of mtDNA experts was assembled within the Mitochondrial Disease Sequence Data Resource Consortium and obtained Expert Panel status from ClinGen. This group reviewed the 2015 American College of Medical Genetics and Association of Molecular Pathology standards and guidelines that are widely used for clinical interpretation of DNA sequence variants and provided further specifications for additional and specific guidance related to mtDNA variant classification. These Expert Panel consensus specifications allow for consistent consideration of the unique aspects of the mtDNA genome that directly influence variant assessment, including addressing mtDNA genome composition and structure, haplogroups and phylogeny, maternal inheritance, heteroplasmy, and functional analyses unique to mtDNA, as well as specifications for utilization of mtDNA genomic databases and computational algorithms.

Abnormal DNA methylation persists throughout carcinogenesis and cancer development. Hence, gene promoter methylation may act as a prognostic tool and provide new potential therapeutic targets for patients with lung adenocarcinoma (LUAD). In this study, to explore prognostic methylation signature, data regarding DNA methylation and RNA-seq, and clinical data of patients with LUAD from the Cancer Genome Atlas database (TCGA) were downloaded. After data preprocessing, the methylation data were divided into training (N = 405) and test sets (N = 62). Then, patients in the training set were assigned to five subgroups based on their different methylation levels using the consensus clustering method. We comprehensively analyzed the survival information, methylation levels, and clinical variables, including American Joint Committee on Cancer (AJCC) stage, tumor-node-metastasis (TNM) staging, age, smoking history, and gender of these five groups. Subsequently, we identified a 16-CpG prognostic signature and constructed a prognostic model, which was verified in the test set. Further analyses showed stable prognostic performance in the stratified cohorts. In conclusion, the new predictive DNA methylation signature proposed in this study may be used as an independent biomarker to assess the overall survival of LUAD patients and provide bioinformatics information for development of targeted therapy.

In an effort to standardize hematopoietic stem cell allograft procedures, the Francophone bone marrow transplantation and Cell Therapy Society (SFGM-TC) organized the 9th Allograft Harmonization Practice Workshop in Lille in September 2018. The purpose of these workshops is to propose a consensual attitude to the centers that wish it. In this workshop, we discuss how to capture the cytogenetic and molecular abnormalities of acute leukaemias, myelomas, myelodysplasias, myeloproliferative syndromes and myelodysplastic/myeloproliferative syndromes in the database common to all European transplant centers called ProMISe and managed by the European Society for Blood and Marrow Transplantation (EBMT). The complexity of cytogenetic and molecular data makes it difficult to enter data into the ProMISe registry. This workshop proposes a tool for input assistance, in tabular form by pathology. The main recommendation for the karyotype remains that of the complex karyotype that must be entered in \"Full caryotype\". Concerning the molecular anomalies, it is necessary to enter all the items proposed by ProMISe. In reviewing all the sheets proposed by ProMise, we note the absence of some relevant elements that can be added later.

BACKGROUND: Sepsis is a serious clinical condition with a poor prognosis, despite improvements in diagnosis and treatment.Therefore, novel biomarkers are necessary that can help with estimating prognosis and improving clinical outcomes of patients with sepsis.
METHODS: The gene expression profiles GSE54514 and GSE63042 were downloaded from the GEO database. DEGs were screened by t test after logarithmization of raw data; then, the common DEGs between the 2 gene expression profiles were identified by up-regulation and down-regulation intersection. The DEGs were analyzed using bioinformatics, and a protein-protein interaction (PPI) survival network was constructed using STRING. Survival curves were constructed to explore the relationship between core genes and the prognosis of sepsis patients based on GSE54514 data.
RESULTS: A total of 688 common DEGs were identified between survivors and non-survivors of sepsis, and 96 genes were involved in survival networks. The crucial genes Signal transducer and activator of transcription 5A (STAT5A), CCAAT/enhancer-binding protein beta (CEBPB), Myc proto-oncogene protein (MYC), and REL-associated protein (RELA) were identified and showed increased expression in sepsis survivors. These crucial genes had a positive correlation with patients\' survival time according to the survival analysis.
CONCLUSIONS: Our findings indicate that the genes STAT5A, CEBPB, MYC, and RELA may be important in predicting the prognosis of sepsis patients.