Comparative assembly and analysis of different sized genomes using Pacbio sequencing technology
Bibliographic record
Abstract
PacBio is the third generation sequencing technology which is based on the single molecule real time sequencing (SMRT) platform using the property of zero-mode waveguide (ZMW). This technology generates very long reads which is best suited for various applications like de novo genome assembly, structural variations, full length transcriptomes, direct detection of base modifications etc. PacBio data can either be used alone or in combination with the illumina based shorter reads to facilitate a good assembly. Different algorithms are available to construct the genome based on PacBio alone or hybrid datasets. In order to identify the best possible approach we did a comparative study employing the widely accepted assembly tools on E.coli, C.elegans and A.thaliana datasets (PacBio & Ilumina (Paired end & Mate Pair)). We performed de novo genome assembly, gene prediction and gene annotation for all possible dataset (PacBio & Illumina PE & MP) and tools combination. The study resulted in the identification of the best method that could assemble the 4.6 MB of E.coli genome covering ~97% of BUSCO represented genes in a single contig. For C.elegans and A.thaliana we were able to achieve 109 MB and 123 MB sized assembly with ~80% of BUSCO represented genes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".