Recent Developments in Using Advanced Sequencing Technologies for the Genomic Studies of Lignin and Cellulose Degrading Microorganisms
Bibliographic record
Abstract
Lignin is a complex polyphenyl aromatic compound which exists in tight associations with cellulose and hemicellulose to form plant primary and secondary cell wall. Lignocellulose is an abundant renewable biomaterial present on the earth. It has gained much attention in the scientific community in recent years because of its potential applications in bio-based industries. Microbial degradation of lignocellulose polymers was well studied in wood decaying fungi. Based on the plant materials they degrade these fungi were classified as white rot, brown rot and soft rot. However, some groups of bacteria belonging to the actinomycetes, α-proteobacteria and β-proteobacteria were also found to be efficient in degrading lignocellulosic biomass but not well understood unlike the fungi. In this review we focus on recent advancements deployed for finding and understanding the lignocellulose degradation by microorganisms. Conventional molecular methods like sequencing 16s rRNA and Inter Transcribed Spacer (ITS) regions were used for identification and classification of microbes. Recent progression in genomics mainly next generation sequencing technologies made the whole genome sequencing of microbes possible in a great ease. The whole genome sequence studies reveals high quality information about genes and canonical pathways involved in the lignin and other cell wall components degradation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".