10x genomics chromium targetted molecule assembly and genome scaffolding
Bibliographic record
Abstract
10X Genomic Chromium sequencing provides long range information between Illumina sequence read pairs. Each read pair is linked to a group of reads by a barcode index within a fragment size typically between 10-100 kbp. This powerful technology has been used in phasing haplotypes, but is difficult to use for assembly and scaffolding due to the low relative coverage of each index and the absence of positional information (ie. read order and position within each fragment). Unlike Illumina TruSeq technology, the linked read pairs are below 1x coverage for each index so they cannot be used to generate synthetic long reads, which would otherwise greatly simplify assembly. Using the Chromium-indexed reads as baits one can use complementary data to fill in gaps, to reconstruct entire fragments. Our method uses previously tagged reads as additional bait sequences to progressively fill-in the missing segments of a fragment of interest, to allow localized assembly. These reconstructed fragments could not only lead to better assemblies downstream, but also help increase the quality of sequence abundance counts on transcriptomic or metagenomic studies, because longer sequences have a higher specificity. In addition to fragment assembly, we have successfully used Chromium information to scaffold existing assemblies. We have developed ARCS (Assembly Roundup by Chromium Scaffolding), an algorithm that uses indexed fragments shared between assembled contigs to order and orient contigs. We show the contiguity of an ABySS human genome assembly can be increased over six-fold, from an N50 of 50 kbp to 303 kbp, using only 25x coverage Chromium data.This method is complementary to upstream assembly methods, as it can scaffold sequences that have systemic missing read coverage due to biases introduced by Illumina sequencing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".