ReviewPharmaceutics2015
Alignment of Short Reads: A Crucial Step for Application of Next-Generation Sequencing Data in Precision Medicine.
Review in Pharmaceutics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- T-rex: standardized analysis of germline variants in whole-exome sequencing trios.Scientific reports · 2026Article
- Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing.Scientific data · 2024Article
- RNA-seq data science: From raw data to effective interpretation.Frontiers in genetics · 2023Review
- MycoSNP: A Portable Workflow for Performing Whole-Genome Sequencing Analysis of Candida auris.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Sequences to Differences in Gene Expression: Analysis of RNA-Seq Data.Methods in molecular biology (Clifton, N.J.) · 2022Review
- NOseq: amplicon sequencing evaluation method for RNA m6A sites after chemical deamination.Nucleic acids research · 2021Article
- Dealing with Pseudogenes in Molecular Diagnostics in the Next Generation Sequencing Era.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment.Scientific reports · 2019Article
- Similarities and differences between variants called with human reference genome HG19 or HG38.BMC bioinformatics · 2019Article
- Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.Communications biology · 2019Article
- SePIA: RNA and small RNA sequence processing, integration, and analysis.BioData mining · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine or personalized medicine has been proposed as a modernized and promising medical strategy. Genetic variants of patients are the key information for implementation of precision medicine. Next-generation sequencing (NGS) is an emerging technology for deciphering genetic variants. Alignment of raw reads to a reference genome is one of the key steps in NGS data analysis. Many algorithms have been developed for alignment of short read sequences since 2008. Users have to make a decision on which alignment algorithm to use in their studies. Selection of the right alignment algorithm determines not only the alignment algorithm but also the set of suitable parameters to be used by the algorithm. Understanding these algorithms helps in selecting the appropriate alignment algorithm for different applications in precision medicine. Here, we review current available algorithms and their major strategies such as seed-and-extend and q-gram filter. We also discuss the challenges in current alignment algorithms, including alignment in multiple repeated regions, long reads alignment and alignment facilitated with known genetic variants.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.