[Re] Computational Replication of SADDLE: Empirical Next-Generation Sequencing Read Partitions and Primer Interaction Distribution in a 384-Plex PCR Panel

[Re] Computational Replication of SADDLE: Empirical Next-Generation Sequencing Read Partitions and Primer Interaction Distribution in a 384-Plex PCR Panel Author: Rashid Kadayil (ORCID: 0009-0009-6398-4557) Target Journal: ReScience C (ISSN 2430-3658, rescience.github.io) Replication Category: Qualified Partial Replication Public GitHub Code Repository: https://github.com/Rashidmstar12/saddle-replication This reproducibility deposit provides the open-source replication package, derived datasets, and automated verification scripts for an independent computational replication of SADDLE (Xie et al., Nature Communications 13, 1881, 2022; DOI: 10.1038/s41467-022-29500-4; PMID: 35410464). The package confirms the published read classification proportions (~43% on-target, ~56% nonspecific, ~1% primer-dimers) across the 384-plex NGS validation dataset (Sample E19, 3,942,385 reads) within 0.86% absolute difference under our pipeline's derived post-filter classified denominator (N = 2,860,664), corroborates the extreme heavy-tailed concentration of primer-dimers (67.57% in a single dominant pair rp213 x rp371), and provides supplementary exploratory benchmarks evaluating SADDLE Badness heuristics against nearest-neighbor thermodynamics and supervised models.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-10
DOI
https://doi.org/10.5281/zenodo.24100086
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

[Re] Computational Replication of SADDLE: Empirical Next-Generation Sequencing Read Partitions and Primer Interaction Distribution in a 384-Plex PCR Panel

Rashid Kadayil
Zenodo (CERN European Organization for Nuclear Research)
Genomics and Phylogenetic Studies
article

[Re] Computational Replication of SADDLE: Empirical Next-Generation Sequencing Read Partitions and Primer Interaction Distribution in a 384-Plex PCR Panel

Rashid Kadayil
article en

Abstract

[Re] Computational Replication of SADDLE: Empirical Next-Generation Sequencing Read Partitions and Primer Interaction Distribution in a 384-Plex PCR Panel Author: Rashid Kadayil (ORCID: 0009-0009-6398-4557) Target Journal: ReScience C (ISSN 2430-3658, rescience.github.io) Replication Category: Qualified Partial Replication Public GitHub Code Repository: https://github.com/Rashidmstar12/saddle-replication This reproducibility deposit provides the open-source replication package, derived datasets, and automated verification scripts for an independent computational replication of SADDLE (Xie et al., Nature Communications 13, 1881, 2022; DOI: 10.1038/s41467-022-29500-4; PMID: 35410464). The package confirms the published read classification proportions (~43% on-target, ~56% nonspecific, ~1% primer-dimers) across the 384-plex NGS validation dataset (Sample E19, 3,942,385 reads) within 0.86% absolute difference under our pipeline's derived post-filter classified denominator (N = 2,860,664), corroborates the extreme heavy-tailed concentration of primer-dimers (67.57% in a single dominant pair rp213 x rp371), and provides supplementary exploratory benchmarks evaluating SADDLE Badness heuristics against nearest-neighbor thermodynamics and supervised models.

Zenodo (CERN European Organization for Nuclear Research)
Genomics and Phylogenetic Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon