| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Topic | Paper title | Year | URL | ||||||||||||||||||||||
2 | Alignment | Flexible seed size enables ultra-fast and accurate read alignment | 2022 | https://www.biorxiv.org/content/10.1101/2021.06.18.449070v3 | ||||||||||||||||||||||
3 | Alignment | Large multiple sequence alignments with a root-to-leaf regressive method | 2019 | https://www.nature.com/articles/s41587-019-0333-6 | ||||||||||||||||||||||
4 | Alignment | Vargas: heuristic-free alignment for assessing linear and graph read aligners | 2020 | https://academic.oup.com/bioinformatics/article/36/12/3712/5823884 | ||||||||||||||||||||||
5 | Alignment | Locality-sensitive hashing for the edit distance | 2020 | https://academic.oup.com/bioinformatics/article/35/14/i127/5529166 | ||||||||||||||||||||||
6 | Alignment | ProbMinHash – A Class of Locality-Sensitive Hash Algorithms for the (Probability) Jaccard Similarity | 2020 | https://ieeexplore.ieee.org/abstract/document/9185081 | ||||||||||||||||||||||
7 | Alignment | On the Hardness of Sequence Alignment on De Bruijn Graphs | 2022 | https://www.liebertpub.com/doi/10.1089/cmb.2022.0411 | ||||||||||||||||||||||
8 | Alignment | Pangenomics enables genotyping of known structural variants in 5202 diverse genomes | 2021 | https://www.science.org/doi/epdf/10.1126/science.abg8871 | ||||||||||||||||||||||
9 | Alignment | Block aligner: fast and flexible pairwise sequence alignment with SIMD-accelerated adaptive blocks | 2021 | https://www.biorxiv.org/content/10.1101/2021.11.08.467651v1 | ||||||||||||||||||||||
10 | Alignment | A fast bit-vector algorithm for approximate string matching based on DP | 1999 | https://dl.acm.org/doi/10.1145/316542.316550 | ||||||||||||||||||||||
11 | Alignment | Introducing difference recurrence relations for faster semi-global alignment of long sequences | 2017 | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2014-8 | ||||||||||||||||||||||
12 | Alignment | An O(NP) Sequence Comparison Algorithm | 1989 | https://www.sciencedirect.com/science/article/abs/pii/002001909090035V?via%3Dihub | ||||||||||||||||||||||
13 | Alignment | Speeding up DP algorithms for finding optimal lattice paths | 1989 | https://epubs.siam.org/doi/10.1137/0149094 | ||||||||||||||||||||||
14 | Alignment | Parameterized syncmer schemes improve long-read mapping | 2022 | https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010638 | ||||||||||||||||||||||
15 | Alignment | Algorithms for Colinear Chaining with Overlaps and Gap Costs | 2022 | https://www.liebertpub.com/doi/10.1089/cmb.2022.0266 | ||||||||||||||||||||||
16 | Alignment | Progressive Cactus is a multiple-genome aligner for the thousand-genome era | 2020 | https://www.nature.com/articles/s41586-020-2871-y | ||||||||||||||||||||||
17 | Alignment / Protein Structure | Protein Structural Alignments From Sequence | 2020 | https://web.archive.org/web/20201209214217id_/https://www.biorxiv.org/content/biorxiv/early/2020/11/04/2020.11.03.365932.full.pdf | ||||||||||||||||||||||
18 | Analysis | on the approximation of the kolmogorow complexity for DNA sequences | 2017 | https://www.researchgate.net/profile/Diogo-Pratas/publication/317104968_On_the_Approximation_of_the_Kolmogorov_Complexity_for_DNA_Sequences/links/5accd4e14585154f3f3f2461/On-the-Approximation-of-the-Kolmogorov-Complexity-for-DNA-Sequences.pdf | ||||||||||||||||||||||
19 | Analysis | Seed-chain-extend alignment is accurate and runs in close to O(m log n) time for similar sequences: a rigorous average-case analysis | 2022 | https://www.biorxiv.org/content/10.1101/2022.10.14.512303v1 | ||||||||||||||||||||||
20 | Applied ML | DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome | 2021 | https://academic.oup.com/bioinformatics/article/37/15/2112/6128680 | ||||||||||||||||||||||
21 | Applied ML | Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning | 2022 | https://www.nature.com/articles/s41591-021-01619-9 | ||||||||||||||||||||||
22 | Applied ML / DNA language modeling | DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome | 2021 | https://academic.oup.com/bioinformatics/article/37/15/2112/6128680 | ||||||||||||||||||||||
23 | Applied ML / homology detection | TM-Vec: template modeling vectors for fast homology detection and alignment | 2022 | https://web.archive.org/web/20220731175100id_/https://www.biorxiv.org/content/biorxiv/early/2022/07/27/2022.07.25.501437.full.pdf | ||||||||||||||||||||||
24 | Applied ML / Pathogenicity Classification | DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks | 2020 | https://pubmed.ncbi.nlm.nih.gov/31298694/ | ||||||||||||||||||||||
25 | Applied ML / Pathogenicity Classification | Interpretable detection of novel human viruses from genome sequencing data | 2021 | https://academic.oup.com/nargab/article/3/1/lqab004/6125551 | ||||||||||||||||||||||
26 | Applied ML / Pathogenicity Classification | Deep learning-based real-time detection of novel pathogens during sequencing | 2021 | https://pubmed.ncbi.nlm.nih.gov/34297793/ | ||||||||||||||||||||||
27 | Applied ML / Pathogenicity Classification | Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection | 2022 | https://academic.oup.com/bioinformatics/article/38/Supplement_2/ii168/6702016 | ||||||||||||||||||||||
28 | Applied ML / protein alignment | DEDAL: A deep learning-based model that learns to align protein sequences and detect homologs | 2022 | https://www.nature.com/articles/s41592-022-01700-2 | ||||||||||||||||||||||
29 | Applied ML / protein function prediction | Structure-based protein function prediction using graph convolutional networks | 2021 | https://www.nature.com/articles/s41467-021-23303-9 | ||||||||||||||||||||||
30 | Applied ML / protein structure | Evolutionary-scale prediction of atomic level protein structure with a language model | 2022 | https://www.biorxiv.org/content/10.1101/2022.07.20.500902v3.abstract | ||||||||||||||||||||||
31 | Applied ML / protein structure prediction | AlphaFold: A highly accurate protein folding algorithm | 2021 | https://www.nature.com/articles/s41586-021-03819-2 | ||||||||||||||||||||||
32 | Applied ML / reference-free enrichment | AMAISE: a machine learning approach to index-free sequence enrichment | 2022 | https://www.nature.com/articles/s42003-022-03498-3 | ||||||||||||||||||||||
33 | Applied ML / Treatment response Prediction | Recurrent somatic mutations as predictors of immunotherapy response | 2022 | https://doi.org/10.1038/s41467-022-31055-3 | ||||||||||||||||||||||
34 | Applied ML / Treatment response Prediction | A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape | 2022 | https://doi.org/10.1186/s13073-022-01024-y | ||||||||||||||||||||||
35 | Applied ML/spatial transcriptomics | Integrative spatial analysis of cell morphologies and transcriptional states with MUSE | 2022 | https://www.nature.com/articles/s41587-022-01251-z | ||||||||||||||||||||||
36 | Applied ML/spatial transcriptomics | Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram | 2021 | https://www.nature.com/articles/s41592-021-01264-7 | ||||||||||||||||||||||
37 | Approximate mapping | Strobealign: flexible seed size enables ultra-fast and accurate read alignment | 2022 | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02831-7 | ||||||||||||||||||||||
38 | Approximate mapping | STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions | 2021 | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02490-0 | ||||||||||||||||||||||
39 | Approximate mapping | mapquik: Efficient low-divergence mapping of long reads in minimizer space | 2022 | https://www.biorxiv.org/content/10.1101/2022.12.23.521809v1?s=31 | ||||||||||||||||||||||
40 | Approximate mapping | Sourmash Branchwater Enables Lightweight Petabyte-Scale Sequence Search | 2022 | https://www.biorxiv.org/content/10.1101/2022.11.02.514947v1 | ||||||||||||||||||||||
41 | Approximate mapping | GSearch: Ultra-Fast and Scalable Microbial Genome Search by combining Kmer Hashing with Hierarchical Navigable Small World Graphs | 2022 | https://www.biorxiv.org/content/10.1101/2022.10.21.513218v1 | ||||||||||||||||||||||
42 | Approximate Mapping | RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes | 2022 | https://www.biorxiv.org/content/10.1101/2023.01.22.525080v1 | ||||||||||||||||||||||
43 | Assembly | Verkko: telomere-to-telomere assembly of diploid chromosomes | 2022 | https://www.biorxiv.org/content/10.1101/2022.06.24.497523v1 | ||||||||||||||||||||||
44 | Assembly | Minimizer-space de Bruijn graphs: Whole-genome assembly of long reads in minutes on a personal computer | 2021 | https://www.sciencedirect.com/science/article/pii/S240547122100332X | ||||||||||||||||||||||
45 | Compression | Masked Minimizers: Unifying sequence sketching methods | 2022 | https://www.biorxiv.org/content/10.1101/2022.10.18.512430v1 | ||||||||||||||||||||||
46 | Compression | Efficient minimizer orders for large values of k using minimum decycling sets | 2022 | https://www.biorxiv.org/content/10.1101/2022.10.18.512682v1 | ||||||||||||||||||||||
47 | Compression | Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time | 2022 | https://www.biorxiv.org/content/10.1101/2022.05.17.492399v3 | ||||||||||||||||||||||
48 | Compression | kmcEx: memory-frugal and retrieval-efficient encoding of counted k-mers | 2019 | https://academic.oup.com/bioinformatics/article/35/23/4871/5481953 | ||||||||||||||||||||||
49 | Compression | High efficiency referential genome compression algorithm | 2019 | https://academic.oup.com/bioinformatics/article-abstract/35/12/2058/5165377 | ||||||||||||||||||||||
50 | Compression | Quark enables semi-reference-based compression of RNA-seq data | 2017 | https://academic.oup.com/bioinformatics/article/33/21/3380/3920524 | ||||||||||||||||||||||
51 | Compression | REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets | 2020 | https://academic.oup.com/bioinformatics/article/36/Supplement_1/i177/5870500 | ||||||||||||||||||||||
52 | Data Structures | Extremely-fast construction and querying of compacted and colored de Bruijn graphs with GGCAT* | 2022 | https://www.biorxiv.org/content/10.1101/2022.10.24.513174v1 | ||||||||||||||||||||||
53 | Data Structures | KMC 2: fast and resource-frugal k-mer counting | 2015 | https://academic.oup.com/bioinformatics/article/31/10/1569/177467 | ||||||||||||||||||||||
54 | Data Structures | Simulating the DNA String Graph in Succinct Space | 2019 | https://arxiv.org/pdf/1901.10453.pdf | ||||||||||||||||||||||
55 | Data Structures | Faster Repetition-Aware Compressed Suffix Trees based on Block Trees | 2019 | https://arxiv.org/abs/1902.03274 | ||||||||||||||||||||||
56 | Data Structures | Space-efficient merging of succinct de Bruijn graphs | 2019 | https://arxiv.org/abs/1902.02889 | ||||||||||||||||||||||
57 | Data Structures | Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters | 2020 | https://dl.acm.org/doi/fullHtml/10.1145/3376122 | ||||||||||||||||||||||
58 | Data Structures / Immunotherapy | NeoSplice: a bioinformatics method for prediction of splice variant neoantigens | 2022 | https://doi.org/10.1093/bioadv/vbac032 | ||||||||||||||||||||||
59 | Genomic variation | Reference flow: reducing reference bias using multiple population genomes | 2021 | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02229-3 | ||||||||||||||||||||||
60 | Genomic variation | A draft human pangenome reference | 2022 | https://www.biorxiv.org/content/10.1101/2022.07.09.499321v1.abstract | ||||||||||||||||||||||
61 | Genomic variation | A general framework for estimating the relative pathogenicity of human genetic variants | 2014 | https://www.nature.com/articles/ng.2892 | ||||||||||||||||||||||
62 | Medical genetics | Mixed-model association for biobank-scale datasets | 2018 | https://www.nature.com/articles/s41588-018-0144-6 | ||||||||||||||||||||||
63 | Medical genetics | Genetic mechanisms of critical illness in COVID-19 | 2020 | https://www.nature.com/articles/s41586-020-03065-y | ||||||||||||||||||||||
64 | Medical genetics | Genetic regulatory variation in populations informs transcriptome analysis in rare disease | 2019 | https://science.sciencemag.org/content/366/6463/351.abstract | ||||||||||||||||||||||
65 | Medical genetics / Mendelian Randomization | Appraising the causal role of smoking in multiple diseases: A systematic review and meta-analysis of Mendelian randomization studies | 2022 | https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(22)00335-8/fulltext | ||||||||||||||||||||||
66 | Medical genetics / Polygenic Risk Scores | Developing and evaluating polygenic risk prediction models for stratified disease prevention | 2018 | https://www.nature.com/articles/nrg.2016.27 | ||||||||||||||||||||||
67 | Microbiome | Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome | 2022 | https://arxiv.org/abs/2205.09906 | ||||||||||||||||||||||
68 | Microbiome | Accurate identification of bacteriophages from metagenomic data using Transformer | 2022 | https://pubmed.ncbi.nlm.nih.gov/35769000/ | ||||||||||||||||||||||
69 | Microbiome | mbImpute: an accurate and robust imputation method for microbiome data | 2021 | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02400-4 | ||||||||||||||||||||||
70 | RNA-Seq Analysis (spatial, scRNAseq) | Spatial transcriptomics at subspot resolution with BayesSpace | 2021 | https://www.nature.com/articles/s41587-021-00935-2 | ||||||||||||||||||||||
71 | RNA-Seq Analysis (spatial, scRNAseq) | Cell2location maps fine-grained cell types in spatial transcriptomics | 2022 | https://doi.org/10.1038/s41587-021-01139-4 | ||||||||||||||||||||||
72 | ||||||||||||||||||||||||||
73 | ||||||||||||||||||||||||||
74 | ||||||||||||||||||||||||||
75 | ||||||||||||||||||||||||||
76 | ||||||||||||||||||||||||||
77 | ||||||||||||||||||||||||||
78 | ||||||||||||||||||||||||||
79 | ||||||||||||||||||||||||||
80 | ||||||||||||||||||||||||||
81 | ||||||||||||||||||||||||||
82 | ||||||||||||||||||||||||||
83 | ||||||||||||||||||||||||||
84 | ||||||||||||||||||||||||||
85 | ||||||||||||||||||||||||||
86 | ||||||||||||||||||||||||||
87 | ||||||||||||||||||||||||||
88 | ||||||||||||||||||||||||||
89 | ||||||||||||||||||||||||||
90 | ||||||||||||||||||||||||||
91 | ||||||||||||||||||||||||||
92 | ||||||||||||||||||||||||||
93 | ||||||||||||||||||||||||||
94 | ||||||||||||||||||||||||||
95 | ||||||||||||||||||||||||||
96 | ||||||||||||||||||||||||||
97 | ||||||||||||||||||||||||||
98 | ||||||||||||||||||||||||||
99 | ||||||||||||||||||||||||||
100 |