Additional file 1 of Genome and transcriptome analysis of rock-dissolving Pseudomonas sp. NLX-4 strain
Bibliographic record
Abstract
Additional file 1: Figure S1. Step-wise pipeline of experiments implemented in our study 1) rock sampling, 2) screening of bacteria, 3) isolating efficient strain, 4) identification of strain, 5) silicate rock-dissolution experiments, 6) NLX-4 strain’s effective secretory compounds, 7) NLX-4 whole-genome sequencing, 8) NLX-4 genome-wide transcriptome and 9) qRT-PCR experiment. Table S1. Indoleacetic acid and siderophore productions of the tested strains. Table S2. The contents of the NLX-4 metabolites associated with rock dissolution. Table S3. The contents of the bacterial metabolites produced by strain NLX-4 cultured with K-bearing rock samples and K+, respectively. Figure S2. a) Cell morphologies of strain NLX-4 (×1000); b) Neighbor-joining phylogenetic tree reconstructed based on 16S rDNA sequences, which showed the phylogenetic relationships between strain NLX-4 and related type bacteria. Bootstrap values (expressed as percentages of 1000 replications) greater than 50% are shown at branch points. The scale bars represent 0.05 substitutions per nucleotide position. Table S4. Data obtained from Illumina HiSeq4000 and PacBio RSII SMRT sequencing systems. Figure S3. The sequencing workflow applied for the Illumina HiSeq 4000 and PacBio RSII sequencing systems. Figure S4. The analysis workflow applied in this study for the assembly of the NLX-4 genome using hybrid analysis approach implementing the genome sequences obtained from Illumina and PacBio systems. Figure S5. The data analysis pipeline used for biological contextualization of the Pseudomonas sp NLX4 strain. Table S5. Lists the final results obtained from the genome assembly and genome biological contextualization. a) results obtained after the RS_HGAP (SMRT analysis suite) and Celera software pipeline, b) gene prediction using Glimmer, c) ncRNA prediction, d) tandem repeat prediction, e) CRISPR finder and f) biological contextualization (GO, COG, InterPro, KEGG, Swiss-Prot and BLAST-NR. Table S6A. List of primers used for qRT-PCR of NLX-4 genes which were differentially expressed in transcriptome analysis. Table S6B. Summary of sequences analysis of Pseudomonas NLX-4 genome. Table S7. a) Summary of mapping to genes; S7b) Summary of mapping to genomes. Figure S6 The GC skewness of the Pseudomonas sp NLX-4 strain and the genome-wide distribution of different cellular process. Figure S7. Shows the distribution and statics of length and mass of the polymerase reads and subreads, respectively. a) The results are represented for both raw reads and clean reads, respectively; b) base composition of data. On the X axis, 1-90 bp represents the base position of read1, and 91-180 bp represents the base position of read; c) shows the reads base mass distribution. Figure S8. a) Correlation analysis of GC content and Depth: The abscissa is the GC content, and the ordinate is the average sequencing depth; b) shows the Kmer Analysis Graph where the abscissa is Depth, and the ordinate is the ratio of the frequency at each depth to the total frequency. Without considering the sequencing error rate, the heterozygosity and repetition of the genome. Figure S9. Gene Prediction glimmers. Figure S10. Birds eye view of the distribution of reads mapped to the reference genome, each figure shows the distribution of genes and also the distribution of reads in the longest 1 chromosome/scaffold. Figure S11. The results of randomness assessment showing the distribution of reads mapped to the reference genome. Figure S12. Distribution of gene’s coverage for individual sequenced samples. Figure S13. Correlation analyses of three biological replicates in treatment and CK groups, respectively. The Pearson correction coefficients are shown in the upper right corner of the plot. Figure S14. Detailed workflow implemented for the illumina RNA-Sequencing and the sequencing analysis pipeline for obtaining for the gene annotations. Figure S15. The enriched and significant KEGG pathways based on their differentially expressed genes and the total number of genes belonging to each pathway. Figure S17. The qRT-PCR analysis of the specifically selected six differentially expressed genes involved in the rock-dissolution process, selected based on the RNA-Seq results. Figure S18. The differentially expressed genes (DEGs) obtained from transcriptome analysis. a) fold change results b) DEGs both up and down-regulated c) volcano plot. Figure S19. a) Total number of novel transcripts discovered based on the transcriptome data analysis, which includes both novel transcripts in coding and non-coding regions; b) Total number of differentially expressed sRNA in control and treatment samples; c) distribution of the total number of sRNA’s and their length; d) Top differentially expressed sRNA’s represented by its candidate-ID along with its start and ending regions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Supplementary file of a bacterial genome and transcriptome paper; a domain data artifact.
This supplementary file documents genomic and transcriptomic experiments on bacteria rather than studying research practice.
Supplementary figures and tables for a Pseudomonas genomics paper; domain biology supporting material.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.652 | 0.116 |
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".