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Jianglin Feng

dblp:255/6503 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2021
0000-0003-3103-5209ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Theoretical computer science
1 paper
Computational geometry · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.922021
IGD: high-performance search for large-scale genomic interval datasets · Bioinform. 2021
Augmented Interval List: a novel data structure for efficient genomic interval search · Bioinform. 2019
Computational geometry › geometric intersection
interval intersection
0.412019
Augmented Interval List: a novel data structure for efficient genomic interval search · Bioinform. 2019

Methods — techniques the papers use, named apart from their topics

linear binning · 0.5
YearPublicationVenuePosition
2021 IGD: high-performance search for large-scale genomic interval datasets
abstract
SUMMARY: Databases of large-scale genome projects now contain thousands of genomic interval datasets. These data are a critical resource for understanding the function of DNA. However, our ability to examine and integrate interval data of this scale is limited. Here, we introduce the integrated genome database (IGD), a method and tool for searching genome interval datasets more than three orders of magnitude faster than existing approaches, while using only one hundredth of the memory. IGD uses a novel linear binning method that allows us to scale analysis to billions of genomic regions. AVAILABILITYAND IMPLEMENTATION: https://github.com/databio/IGD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Jianglin Feng, Nathan C. Sheffield
Bioinform.1
2019 Augmented Interval List: a novel data structure for efficient genomic interval search
abstract
MOTIVATION: Genomic data is frequently stored as segments or intervals. Because this data type is so common, interval-based comparisons are fundamental to genomic analysis. As the volume of available genomic data grows, developing efficient and scalable methods for searching interval data is necessary. RESULTS: We present a new data structure, the Augmented Interval List (AIList), to enumerate intersections between a query interval q and an interval set R. An AIList is constructed by first sorting R as a list by the interval start coordinate, then decomposing it into a few approximately flattened components (sublists), and then augmenting each sublist with the running maximum interval end. The query time for AIList is O(log2N+n+m), where n is the number of overlaps between R and q, N is the number of intervals in the set R and m is the average number of extra comparisons required to find the n overlaps. Tested on real genomic interval datasets, AIList code runs 5-18 times faster than standard high-performance code based on augmented interval-trees, nested containment lists or R-trees (BEDTools). For large datasets, the memory-usage for AIList is 4-60% of other methods. The AIList data structure, therefore, provides a significantly improved fundamental operation for highly scalable genomic data analysis. AVAILABILITY AND IMPLEMENTATION: An implementation of the AIList data structure with both construction and search algorithms is available at http://ailist.databio.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Jianglin Feng, Aakrosh Ratan, Nathan C. Sheffield
Bioinform.1