A software tool ‘CroCo’ detects pervasive cross-species contamination in next generation sequencing data
Bibliographic record
Abstract
BACKGROUND: Multiple RNA samples are frequently processed together and often mixed before multiplex sequencing in the same sequencing run. While different samples can be separated post sequencing using sample barcodes, the possibility of cross contamination between biological samples from different species that have been processed or sequenced in parallel has the potential to be extremely deleterious for downstream analyses. RESULTS: We present CroCo, a software package for identifying and removing such cross contaminants from assembled transcriptomes. Using multiple, recently published sequence datasets, we show that cross contamination is consistently present at varying levels in real data. Using real and simulated data, we demonstrate that CroCo detects contaminants efficiently and correctly. Using a real example from a molecular phylogenetic dataset, we show that contaminants, if not eliminated, can have a decisive, deleterious impact on downstream comparative analyses. CONCLUSIONS: Cross contamination is pervasive in new and published datasets and, if undetected, can have serious deleterious effects on downstream analyses. CroCo is a database-independent, multi-platform tool, designed for ease of use, that efficiently and accurately detects and removes cross contamination in assembled transcriptomes to avoid these problems. We suggest that the use of CroCo should become a standard cleaning step when processing multiple samples for transcriptome sequencing.
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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".