Journal: Cell

Article Title: Microbial ecosystems and ecological driving forces in the deepest ocean sediments.

doi: 10.1016/j.cell.2024.12.036

Figure Lengend Snippet: Figure 1. Sampling and extraordinary nov- elty of the Deepest Ocean microbiome re- vealed by the MEER dataset (A) Sampling sites of hadal zones using the hu- man-occupied vehicle (HOV) Fendouzhe in this study, including the Philippine Basin (PB), the Yap Trench (YT), and the Mariana Trench (MT), as well as the bottom (Bt), northern slope (NS), and southern slope (SS) within the Mariana Trench. (B) Comparative analysis between the species- level representative genomes (SRGs) from the MEER dataset and the species-level genomes in the Ocean Microbiomics Database (OMD), the typical deep-sea and hadal sediment. The OMD included isolated reference genome (ISO) of Global Ocean Reference Genomes (GORG), sin- gle-cell amplified genome (SAG) of MAR Database (MARD), as well as seawater metagenomes of abyssopelagic layer (ABY, 4,500–6,000 m), bathypelagic layer (BAT, 1,000–4,500 m), meso- pelagic layer (MES, 200–1,000 m), and epipelagic layer (EPI, 0–200 m). Doughnut charts illustrate the sample habitat distribution in each habitat. Bar plots depict the distribution of MEER SRGs de- tected in reference habitats. (C) Taxonomic novelty of the MEER dataset against the Genome Taxonomy Database (GTDB, release 220). Light bars extending leftward indi- cate the finest taxonomic level to which SRGs or 16S rRNA gene amplicon sequence variants (ASVs) can be annotated. Solid bars extending rightward represent unreported taxa at each taxonomic level. (D) De novo phylogenetic tree of SRGs, color- coded by the 10 most abundant phyla in the MEER dataset. Purple shapes indicate the placement of three bacterial SRGs that could not be assigned to known phyla according to GTDB, with subtrees showing their neighboring clades and relative evolutionary divergence (RED) values. Note that the bacterial and archaeal trees were combined for visualization purpose but were not rooted together. (E) Distribution of novelty among SRGs and 16S rRNA gene ASVs across the 10 most abundant phyla. Stacked bar plots represent the number of unreported and known SRGs or ASVs, while or- ange lines and dots indicate the percentage of novelty. See also Figure S1 and Tables S1 and S2.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples Sediment samples Collected from the Mariana Trench, the Yap Trench and the Philippine Basin MEER Critical commercial assays MGIEasy Environmental Microbiome DNA Extraction Kit MGI-Tech, China Cat#940-001731-00 MGIEasy Fast FS DNA Library Prep Set MGI-Tech, China Cat#940-000030-00 Deposited data Raw data This paper CNSA: CNP0004890 Raw data This paper NODE: OEP004067 Shotgun metagenome-derived specieslevel representative genomes This paper eLMSG: LMSG_G000037053.1 – LMSG_G000044616.1 Source code for quality control, assembly, binning, classification, and abundance profiling This paper https://github.com/meer-trench/genome_ catalogue Source code for ecological analyses This paper https://doi.org/10.5281/zenodo.13317475 Ocean Microbiomics Database Paoli et al.30 https://microbiomics.io/ocean/ GTDB database release 220 Parks et al.49 https://gtdb.ecogenomic.org/ SILVA database version 138 Pruesse et al.79 https://www.arb-silva.de/ Greengenes2 database release 2022.10 McDonald et al.52 https://ftp.microbio.me/greengenes_ release/2022.10/ Oligonucleotides 515F (Parada), Forward 5’-GTGYCAGCMGCCGCGGTAA-3’ N/A 806R (Apprill), Reverse 5’-GGACTACNVGGGTWTCTAAT-3’ N/A Software and algorithms QIMME2 Bolyen et al.80 https://github.com/qiime2 RESCRIPt (2024.10.0.dev0+7.g3c179ca) Robeson et al.81 https://github.com/bokulich-lab/RESCRIPt Cutadapt Martin et al.82 https://cutadapt.readthedocs.io/en/stable/ DADA2 Callahan et al.83 https://github.com/benjjneb/dada2 fastp (v0.23.2) Chen et al.84 https://github.com/OpenGene/fastp MEGAHIT (v1.2.9) Li et al.85 https://github.com/voutcn/megahit MetaBAT2 (v2.12.1) Kang et al.86 https://bitbucket.org/berkeleylab/metabat BWA (v0.7.17-r1188) Li et al.87 https://github.com/lh3/bwa dRep (v3.4.0) Olm et al.76 https://github.com/MrOlm/drep CoverM (v0.7.0) N/A https://github.com/wwood/CoverM GTDB-Tk (v2.4.0) Chaumeil et al.88 https://github.com/Ecogenomics/GTDBTk Prodigal Hyatt et al.89 https://github.com/hyattpd/Prodigal Mantis Queirós et al.90 https://github.com/PedroMTQ/mantis R (v4.1.0) R Project https://www.r-project.org/ R Studio Server v2023.12.1.402 Posit https://posit.co/products/open-source/ rstudio-server/ ggtree (v3.10.1) Yu et al.91 https://github.com/YuLab-SMU/ggtree ggtreeExtra (v1.12.0) Xu et al.92 https://github.com/YuLab-SMU/ ggtreeExtra vegan (v2.6-4) Oksanen et al.93 https://github.com/vegandevs/vegan (Continued on next page) e1 Cell 188, 1363–1377.e1–e3, March 6, 2025

Techniques: Sampling, Northern Blot, Isolation, Amplification, Sequencing

Journal: Current opinion in microbiology

Article Title: Bacterial genetics and molecular pathogenesis in the age of high throughput DNA sequencing

doi: 10.1016/j.mib.2020.01.007

Figure Lengend Snippet: Applications of high throughput sequencing in bacterial genetics. (a) Whole genome sequencing is often the first step in the analysis of gene function. DNA sequencing can be accomplished using Illumina, SMRT, or ONT sequencing technologies. Depending on the size of a genome, multiple isolates can be multiplexed and sequenced in parallel while still obtaining high coverage. Once a genome is assembled, it can be used for comparative genomics or in other downstream applications. (b) Genome sequencing with SMRT also captures epigenetic modifications such as methylation. The high-resolution detection of modified bases can be used to determine a strains methylome. (c) Transposon insertion sequencing combines transposon mutagenesis with high throughput sequencing. Mutants present in the input and output pools are identified by mapping the insertion sites back to a reference genome. Insertion in genes that disappear from the input pool are considered conditionally essential. (d) RNA-seq uses Illumina sequencing to sequence cDNA. Sequenced reads are mapped back to a reference genome, capturing information about gene expression, transcriptional start sites, operon structure, and small RNAs. (e) Chemical mutagenesis enables rapid genetic analysis in genetically intractable bacteria. This is a particularly useful approach for novel bacterial isolates without established genetic tools. Mutants are generated with chemical mutagens and subjected to phenotypic screening. A pool of mutants enriched for the phenotype of interest is then sequenced and the mutations are mapped to a reference genome.

Article Snippet: The options for microbial whole genome sequencing (WGS) include platforms for shorts reads, primarily Illumina, as well as ‘third generation’ DNA sequencing platforms, such as PacBio’s single molecule real-time sequencing (SMRT) [ 1 ] and Oxford Nanopore Technology (ONT) [ 2•• ].

Techniques: Next-Generation Sequencing, Sequencing, DNA Sequencing, Methylation, Modification, Mutagenesis, RNA Sequencing, Illumina Sequencing, Gene Expression, Bacteria, Generated

Journal: JAMA

Article Title: Next Generation Sequencing of Infectious Pathogens

doi: 10.1001/jama.2018.21669

Figure Lengend Snippet: [Slide 1] Workflow transforming pathogen genome sequence data into actionable information.

Article Snippet: Although microbial genomes are generally smaller and less complex than human genomes, long-read sequencing technologies (such as single-molecule real-time (SMRT) sequencing, Pacific Biosciences, Menlo Park, CA) are useful for constructing complete, highly accurate genomes and sorting out plasmids, repeats, and other complex regions.

Techniques: Sequencing