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1. Insights from Student Solutions to MongoDB Homework Problems

   https://doi.org/10.1145/3430665.3456308  

   Alkhabaz, Ridha; Poulsen, Seth; Chen, Mei; Alawini, Abdussalam (June 2021, Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)

   null (Ed.)

   We analyze submissions for homework assignments of 527 students in an upper-level database course offered at the University of Illinois at Urbana-Champaign. The ability to query databases is becoming a crucial skill for technology professionals and academics. Although we observe a large demand for teaching database skills, there is little research on database education. Also, despite the industry's continued demand for NoSQL databases, we have virtually no research on the matter of how students learn NoSQL databases, such as MongoDB. In this paper, we offer an in-depth analysis of errors committed by students working on MongoDB homework assignments over the course of two semesters. We show that as students use more advanced MongoDB operators, they make more Reference errors. Additionally, when students face a new functionality of MongoDB operators, such as texttt$group operator, they usually take time to understand it but do not make the same errors again in later problems. Finally, our analysis suggests that students struggle with advanced concepts for a comparable amount of time. Our results suggest that instructors should allocate more time and effort for the discussed topics in our paper. 

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2. Guidelines for gene and genome assembly nomenclature

   https://doi.org/10.1093/genetics/iyaf006  

   Cannon, Ethalinda KS; Molik, David C; Wright, Adam J; Zhang, Huiting; Honaas, Loren; Chougule, Kapeel; Dyer, Sarah (January 2025, GENETICS)

   Harris, T (Ed.)

   Abstract The rapid increase in the number of reference-quality genome assemblies presents significant new opportunities for genomic research. However, the absence of standardized naming conventions for genome assemblies and annotations across datasets creates substantial challenges. Inconsistent naming hinders the identification of correct assemblies, complicates the integration of bioinformatics pipelines, and makes it difficult to link assemblies across multiple resources. To address this, we developed a specification for standardizing the naming of reference genome assemblies, to improve consistency across datasets and facilitate interoperability. This specification was created with FAIR (Findable, Accessible, Interoperable, and Reusable) practices in mind, ensuring that reference assemblies are easier to locate, access, and reuse across research communities. Additionally, it has been designed to comply with primary genomic data repositories, including members of the International Nucleotide Sequence Database Collaboration consortium, ensuring compatibility with widely used databases. While initially tailored to the agricultural genomics community, the specification is adaptable for use across different taxa. Widespread adoption of this standardized nomenclature would streamline assembly management, better enable cross-species analyses, and improve the reproducibility of research. It would also enhance natural language processing applications that depend on consistent reference assembly names in genomic literature, promoting greater integration and automated analysis of genomic data. This is a good time to consider more consistent genomic data nomenclature as many research communities and data resources are now finding themselves juggling multiple datasets from multiple data providers. 

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3. Persistent Gaps and Errors in Reference Databases Impede Ecologically Meaningful Taxonomy Assignments in 18S rRNA Studies: A Case Study of Terrestrial and Marine Nematodes

   https://doi.org/10.1002/edn3.70080  

   De_Santiago, Alejandro; Pereira, Tiago José; Ferrero, Timothy John; Barnes, Natalie; Lallias, Delphine; Creer, Simon; Bik, Holly M (March 2025, Environmental DNA)

   ABSTRACT In metabarcoding studies, Linnaean taxonomy assignments of Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) underpin many downstream bioinformatics analyses and ecological interpretations of environmental DNA (eDNA) datasets. However, public molecular databases (i.e., SILVA, EUKARYOME, BOLD) for most microbial metazoan phyla (nematodes, tardigrades, kinorhynchs, etc.) are sparsely populated, negatively impacting our ability to assign ecologically meaningful taxonomy to these understudied groups. Additionally, the choice of bioinformatics parameters and computational algorithms can further affect the accuracy of eDNA taxonomy assignments. Here, we use twoin silicodatasets to show that taxonomy assignments using the 18S rRNA gene can be dramatically improved by curating Linnaean taxonomy strings associated with each reference sequence and closing phylogenetic gaps by improving taxon sampling. Using free‐living nematodes as a case study, we applied two commonly used taxonomy assignment algorithms (BLAST+ and the QIIME2 Naïve Bayes classifier) across six iterations of the SILVA 138 reference database to evaluate the precision and accuracy of taxonomy assignments. The BLAST+ top hit with a 90% sequence similarity cutoff often returned the highest percentage of correctly assigned taxonomy at the genus level, and the QIIME2 Naïve Bayes classifier performed similarly well when paired with a reference database containing corrected taxonomy strings. Our results highlight the urgent need for phylogenetically informed expansions of public reference databases (encompassing both genomes and common gene markers), focused on poorly sampled lineages that are now robustly recovered via eDNA metabarcoding approaches. Additional taxonomy curation efforts should be applied to popular reference databases such as SILVA, and taxon sampling could be rapidly improved by more frequent incorporation of newly published GenBank sequences linked to genus‐ and/or species‐level identifications. 

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4. Detecting and correcting misclassified sequences in the large-scale public databases

   https://doi.org/10.1093/bioinformatics/btaa586  

   Bagheri, Hamid; Severin, Andrew; Rajan, Hridesh; Elofsson, Arne (June 2020, Bioinformatics)

   Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the non-redundant (NR) database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than 2 million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability Source code, dataset, documentation, Jupyter notebooks, and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. Shotgun metagenomics captures more microbial diversity than targeted 16S rRNA gene sequencing for field specimens and preserved museum specimens

   https://doi.org/10.1371/journal.pone.0291540  

   Madison, Joseph D; LaBumbard, Brandon C; Woodhams, Douglas C (September 2023, PLOS ONE)

   Kalendar, Ruslan (Ed.)

   The use of museum specimens for research in microbial evolutionary ecology remains an under-utilized investigative dimension with important potential. Despite this potential, there remain barriers in methodology and analysis to the wide-spread adoption of museum specimens for such studies. Here, we hypothesized that there would be significant differences in taxonomic prediction and related diversity among sample type (museum or fresh) and sequencing strategy (medium-depth shotgun metagenomic or 16S rRNA gene). We found dramatically higher predicted diversity from shotgun metagenomics when compared to 16S rRNA gene sequencing in museum and fresh samples, with this differential being larger in museum specimens. Broadly confirming these hypotheses, the highest diversity found in fresh samples was with shotgun sequencing using the Rep200 reference inclusive of viruses and microeukaryotes, followed by the WoL reference database. In museum-specimens, community diversity metrics also differed significantly between sequencing strategies, with the alpha-diversity ACE differential being significantly greater than the same comparisons made for fresh specimens. Beta diversity results were more variable, with significance dependent on reference databases used. Taken together, these findings demonstrate important differences in diversity results and prompt important considerations for future experiments and downstream analyses aiming to incorporate microbiome datasets from museum specimens. 

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