Data collection for Tsuji et al., 2020, Type I photosynthetic reaction center in an anoxygenic phototrophic member of the Chloroflexota
Bibliographic record
Abstract
Supplementary data files associated with Tsuji et al., 2021, "Type I photosynthetic reaction center in an anoxygenic phototrophic member of the Chloroflexota". These files are used by code in a corresponding GitHub repository (https://github.com/jmtsuji/Ca-Chlorohelix-allophototropha-RCI) that shows how various analyses that are presented in the paper were conducted. Files included: Capt_S15_sequencer_data_raw.tar.gz -- Gzipped tarball containing the raw Illumina MiSeq output data for the 'Candidatus Chlorohelix allophototropha' subculture 15 sequencing run. The run represents a read cloud sequencing run relying on TELL-Seq technology. Indices can be parsed directly from raw output data using the Tell-Read pipeline. scaffold.full.fasta.gz -- the assembled scaffolds generated using Tell-Read and Tell-Link on the above raw MiSeq output data. Ca_Chx_allophototropha_L227-S17_prokka_ORFs.faa.gz and Ca_Chloroheliaceae_bin_L227_5C_prokka_ORFs.faa.gz -- predicted open reading frames (ORFs) from the curated genomes of 'Candidatus Chlorohelix allophototropha' and 'Candidatus Chloroheliales bin L227-5C', respectively. These ORFs were predicted using prokka and were used for the analyses presented in the paper. They are similar to, but differ somewhat from, the ORFs predicted by the NCBI gene annotation pipeline that was used upon uploading the genomes to the NCBI Genbank database. Thus, these original ORF files are provided for reference in case comparison is ever needed to the publicly accessible Genbank files. I_TASSER_homology_models_full_output.tar.gz -- Gzipped tarball containing the full output from I-TASSER for homology models of key phototrophy-related genes encoded by 'Candidatus Chlorohelix allophototropha' and 'Candidatus Chloroheliales bin L227-5C'. After unpacking the tarball, view a summary of the I-TASSER output for each gene by clicking on the 'index.html' file in that gene's folder. lake_survey_MAGs.tar.gz -- Gzipped tarball containing the full collection of 756 metagenome-assembled genomes (MAGs) recovered from the Boreal Shield lake survey, corresponding to those mentioned in Supplementary Data 3. The FastA nucleotide genome sequences, FastA nucleotide predicted protein-coding gene sequences, FastA amino acid predicted protein sequences, and Genome Flat Files (GFFs) for all genomes are provided in the fna, ffn, faa, and gff subdirectories, respectively. lake_survey_MAGs_eggnog_annotations.tar.gz -- Gzipped tarball containing annotations (produced via EggNOG) for all predicted proteins among the 756 MAGs recovered from lake metagenome data. Because proteins were pre-clustered prior to annotation, a "orf2gene" file inside the tarball maps the gene clusters to the ORF IDs used for each genome. lake_survey_MAGs_featureCounts.tsv.gz -- GZipped tab-separated table containing the mapping statistics of metatranscriptome reads on all protein-coding genes from the 756 MAGs recovered from lake metagenome data. lake_survey_Ca_Chloroheliales_MAGs_info.tar.gz -- A subset of information from the previous three files specific to genome bins ELA319 and ELA729, which represent RCI-encoding "Ca. Chloroheliales" members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.278 | 0.145 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".