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#sequencing

1 post1 participant0 posts today
Public
Daniel Hoffmann 🥬

Whole genome #sequencing has become cheap enough to become a routine early diagnostic test for #rareDiseases. This can help patients to avoid years of fruitless diagnostic odyssey. Could also facilitate development of cures. Moving case report. nature.com/articles/d41586-025

www.nature.comWhole-genome sequencing susses out rare diseasesConventional tests that look only at a small subset of genetic code often miss variations hiding outside the protein-coding genome.
Public
naturepoker

What do #nanopore #sequencing #bioinformatics folks think about this one? New preprint on hashing raw nanopore signals and using them directly for de-novo assembly.

arxiv.org/abs/2503.02997v1

Hypothetical blow5-to-assembly pipe would be interesting, though figuring it out is way out of my depth.

arXiv logo
arXiv.orgEnabling Fast, Accurate, and Efficient Real-Time Genome Analysis via New Algorithms and TechniquesThe advent of high-throughput sequencing technologies has revolutionized genome analysis by enabling the rapid and cost-effective sequencing of large genomes. Despite these advancements, the increasing complexity and volume of genomic data present significant challenges related to accuracy, scalability, and computational efficiency. These challenges are mainly due to various forms of unwanted and unhandled variations in sequencing data, collectively referred to as noise. In this dissertation, we address these challenges by providing a deep understanding of different types of noise in genomic data and developing techniques to mitigate the impact of noise on genome analysis. First, we introduce BLEND, a noise-tolerant hashing mechanism that quickly identifies both exactly matching and highly similar sequences with arbitrary differences using a single lookup of their hash values. Second, to enable scalable and accurate analysis of noisy raw nanopore signals, we propose RawHash, a novel mechanism that effectively reduces noise in raw nanopore signals and enables accurate, real-time analysis by proposing the first hash-based similarity search technique for raw nanopore signals. Third, we extend the capabilities of RawHash with RawHash2, an improved mechanism that 1) provides a better understanding of noise in raw nanopore signals to reduce it more effectively and 2) improves the robustness of mapping decisions. Fourth, we explore the broader implications and new applications of raw nanopore signal analysis by introducing Rawsamble, the first mechanism for all-vs-all overlapping of raw signals using hash-based search. Rawsamble enables the construction of de novo assemblies directly from raw signals without basecalling, which opens up new directions and uses for raw nanopore signal analysis.
Public
Jyoti Mishra

Every now and then, I look at trackers and then I come to my senses and then go nope.

More power to you if you like your vibrato values in hex but very, very nope.

I mean, they're a step forward from the MC4 and 8 but that's about it.

#DAWs#Sequencing#CVGate
Public
Healthcare Tech Robot

DATE: February 24, 2025 at 09:00AM
SOURCE: BioWorld MedTech

Direct article link at end of text block below.

.@Roche looks to grow with #sequencing by expansion tech

t.co/tIO4Gf7hGr

#medtech @illumina

Here are any URLs found in the article text:

t.co/tIO4Gf7hGr

#medtech

Articles can be found by scrolling down the page at bioworld.com/topics/85-bioworl .

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NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
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Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: nationalpsychologist.com
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t.coRoche looks to grow with sequencing by expansion techBy Annette Boyle
#healthcare#healthtech#healthcaretech
Public
Healthcare Tech Robot

DATE: February 21, 2025 at 07:00PM
SOURCE: BioWorld MedTech

Direct article link at end of text block below.

.@Roche looks to grow with #sequencing by expansion tech

t.co/tIO4Gf7hGr

#medtech @illumina

Here are any URLs found in the article text:

t.co/tIO4Gf7hGr

#medtech

Articles can be found by scrolling down the page at bioworld.com/topics/85-bioworl .

-------------------------------------------------

Private, vetted email list for mental health professionals: clinicians-exchange.org
.
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
.
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: nationalpsychologist.com
.
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE:
subscribe-article-digests.clin
.
READ ONLINE: read-the-rss-mega-archive.clin
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It's primitive... but it works... mostly...
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t.coRoche looks to grow with sequencing by expansion techBy Annette Boyle
#healthcare#healthtech#healthcaretech
Public
:rss: Hacker News

Static search trees: faster than binary search
curiouscoding.nl/posts/static-
#ycombinator #blog #developer #phd #computational_biology #dna #sequencing #personal #bmi #eth #ethz #eth_zurich #pairwise_alignment

CuriousCoding · Static search trees: 40x faster than binary searchTable of Contents 1 Introduction 1.1 Problem statement 1.2 Motivation 1.3 Recommended reading 1.4 Binary search and Eytzinger layout 1.5 Hugepages 1.6 A note on benchmarking 1.7 Cache lines 1.8 S-trees and B-trees 2 Optimizing find 2.1 Linear 2.2 Auto-vectorization 2.3 Trailing zeros 2.4 Popcount 2.5 Manual SIMD 3 Optimizing the search 3.1 Batching 3.2 Prefetching 3.3 Pointer arithmetic 3.3.1 Up-front splat 3.3.2 Byte-based pointers 3.3.3 The final version 3.4 Skip prefetch 3.5 Interleave 4 Optimizing the tree layout 4.1 Left-tree 4.2 Memory layouts 4.3 Node size \(B=15\) 4.3.1 Data structure size 4.4 Summary 5 Prefix partitioning 5.1 Full layout 5.2 Compact subtrees 5.3 The best of both: compact first level 5.4 Overlapping trees 5.5 Human data 5.6 Prefix map 5.7 Summary 6 Multi-threaded comparison 7 Conclusion 7.1 Future work 7.1.1 Branchy search 7.1.2 Interpolation search 7.1.3 Packing data smaller 7.1.4 Returning indices in original data 7.1.5 Range queries 7.1.6 Sorting queries 7.1.7 Suffix array searching In this post, we will implement a static search tree (S+ tree) for high-throughput searching of sorted data, as introduced on Algorithmica. We’ll mostly take the code presented there as a starting point, and optimize it to its limits. For a large part, I’m simply taking the ‘future work’ ideas of that post and implementing them. And then there will be a bunch of looking at assembly code to shave off all the instructions we can. Lastly, there will be one big addition to optimize throughput: batching.
Public
:rss: Hacker News

Static search trees: 40x faster than binary search
curiouscoding.nl/posts/static-
#ycombinator #blog #developer #phd #computational_biology #dna #sequencing #personal #bmi #eth #ethz #eth_zurich #pairwise_alignment

CuriousCoding · Static search trees: 40x faster than binary searchTable of Contents 1 Introduction 1.1 Problem statement 1.2 Motivation 1.3 Recommended reading 1.4 Binary search and Eytzinger layout 1.5 Hugepages 1.6 A note on benchmarking 1.7 Cache lines 1.8 S-trees and B-trees 2 Optimizing find 2.1 Linear 2.2 Auto-vectorization 2.3 Trailing zeros 2.4 Popcount 2.5 Manual SIMD 3 Optimizing the search 3.1 Batching 3.2 Prefetching 3.3 Pointer arithmetic 3.3.1 Up-front splat 3.3.2 Byte-based pointers 3.3.3 The final version 3.4 Skip prefetch 3.5 Interleave 4 Optimizing the tree layout 4.1 Left-tree 4.2 Memory layouts 4.3 Node size \(B=15\) 4.3.1 Data structure size 4.4 Summary 5 Prefix partitioning 5.1 Full layout 5.2 Compact subtrees 5.3 The best of both: compact first level 5.4 Overlapping trees 5.5 Human data 5.6 Prefix map 5.7 Summary 6 Multi-threaded comparison 7 Conclusion 7.1 Future work 7.1.1 Branchy search 7.1.2 Interpolation search 7.1.3 Packing data smaller 7.1.4 Returning indices in original data 7.1.5 Range queries 7.1.6 Sorting queries 7.1.7 Suffix array searching In this post, we will implement a static search tree (S+ tree) for high-throughput searching of sorted data, as introduced on Algorithmica. We’ll mostly take the code presented there as a starting point, and optimize it to its limits. For a large part, I’m simply taking the ‘future work’ ideas of that post and implementing them. And then there will be a bunch of looking at assembly code to shave off all the instructions we can. Lastly, there will be one big addition to optimize throughput: batching.
Public
naturepoker

For sequencing curious folks: a lab is trying to use up their last NextSeq 550 flowcell, and they're willing to run it for anyone for cost of the flowcell or less (likely a lot less).

Anyone interested in 30~39Gb worth of 150bp reads, let me know! We have about a week to figure this out.

Amateurs and independent researchers welcome too, but you'll have to extract your own DNA and send it to us.

#bioinformatics#sequencing#microbiology
Public *
Leszek

VERY excited to share SeqTagger with the world! github.com/novoalab/SeqTagger/

If you want to learn more about our super-fast and accurate #demultiplexing tool for #direct #RNA #sequencing (DRS), check out the thread below. #nanopore
biorxiv.org/content/10.1101/20

We started developing SeqTagger which follows a very straightforward logic: Let’s demultiplex by basecalling the #DNA #barcode sequence (on an RNA pore) and aligning it to a set of reference barcode sequences.

Schematic overview of the direct RNA sequencing (DRS) demultiplexing workflow. (A) Overview of the barcoded direct RNA sequencing workflow in which the standard reverse transcription adapter (RTA) is replaced with a barcode-containing adapter. Following adapter ligation and reverse transcription the RNA ligation adapter (RLA), containing the helicase enzyme, is ligated, making the library sequencing-ready. (B) Overview of the demultiplexing workflow performed by SeqTagger. The algorithm first segments the raw current intensity signal by identifying the poly-(A)-tail signal to extract the barcode-containing RT adapter. Following signal normalisation, the DNA sequence is basecalled and aligned to a set of reference barcodes. Finally, a filtering step is applied based on the median base quality (BaseQ) to remove misassigned barcode sequences.
Public
Giuseppe Michieli

#Detection and full #genome #sequencing of a #Deltacoronavirus and other bird associated #viruses from #feces of the kelp #gull (Larus dominicanus) sampled at the South Shetland Islands #Antarctica.

Source: BioRxIV, AbstractBird species are known to be the main reservoir of a range of respiratory viruses such as Influenza, Newcastle and Coronaviruses. Migratory birds are particularly important for the maintenance and long distance spread of the virus to wild bird and…

etidioh.wordpress.com/2024/10/

ETIDIoH · #Detection and full #genome #sequencing of a #Deltacoronavirus and other bird associated #viruses from #feces of the kelp #gull (Larus dominicanus) sampled at the South Shetland Islands #Antarctica.
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