each step of the way, the agent reassesses its plan of action and self-corrects, allowing for informed decision-making.

scaffold information generated by Bambus 2 allows us to integrate multiple sources of information and obtain more accurate annotations of the resulting assembly

Unlike MetAMOS, SmashCommunity only supports a small set of assembly and analysis tools

simply links together the individual analysis tools

provide additional functionality made possible by the integration of different analyses

compare its performance to other software tools

'Assembly mode', which requires larger amounts of RAM and starts from raw read data

We intended to encourage users to tailor MetAMOS to the biological questions they want to answer, not the inverse

customize their own pipelines by combining the modules they deem necessary

Ruffus [29]) to track inputs/outputs/states and checkpoint while running through computationally intensive analyses.

INSTALL script. This will automatically configure the pipeline to run within the user's environment and also fetch all required data

initPipeline is mainly involved with creating a project environment, and describing input files

runPipeline takes a project directory as the input and will initiate execution of the entire MetAMOS pipeline

MetAMOS pipeline ends by generating an interactive, HTML summary

assembly statistics and estimated abundance information

commercial 16S bioinformatic pipeline from 1928 Diagnostics (1928-16S) was evaluated and compared with the open-sourced gms_16S pipeline that is based on the EMU classification tool (GMS-16S).

RapidONT, a workflow designed for cost-effective and accessible WGS-based pathogen analysis

routine clinical adoption of WGS is hindered by factors such as high costs, technical complexity, and the requirement for bioinformatics expertise for data analysis

user-friendly web-based platform Pathogenwatch, which facilitates species identification, molecular typing, and antimicrobial resistance (AMR) prediction

anvi’o empowers its users to navigate through ‘omics data without imposing rigid workflows.

users still needing considerable expertise to interpret the results.

inter-dependencies of the data types and the various data formats that need to ‘talk’ to each other.

Assembly graphs produced by different tools from the same data may differ significantly, posing a challenge to tools for downstream processing tasks

choice of the right algorithm for a given dataset has become difficult due to numerous comparative reports on these different assemblers [88, 89]

most widely used assemblers are MegaHit, metaSPAdes, RayMeta and IDBA-UD

The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics

assembly, binning, annotation and visualization

best-practice protocols

major advantage of De Bruijn graphs is that assembled reads contain fewer errors and errors can be easily corrected prior to assembly

A Modular, Open-Source whole genome assembler.

human mtDNA is an extranuclear molecule of ∼16.5 kilobases

mtDNA is an informative matrilineal uniparental marker that can be used to trace the ancestry of an individual

scanned all the assembled contigs from each sample for human mtDNA by running homology search (i.e., BLASTn)

Positive cases were derived from stool, oral, and skin samples

it is now considered mandatory to remove human DNA (or RNA) sequencing reads before depositing metagenomes in public repositories

Human DNA could be considered personal identifying information

there is not a consensus on which version of the human reference genome to use for human DNA decontamination

studies that reported exclusion of only reads where both paired-end reads are mapped still detected mtDNA

small, circular nature of the mitochondrial genome allows reads to span the start and end positions, leading to incomplete exclusion of mtDNA

nuclear DNA is linear and much larger, so this approach effectively removes most nuclear DNA reads, leaving only minimal traces

exclude more off-target reads

using single-end mapping

extensive validation of the available pipelines is still required.

MADRe, a modular and scalable pipeline for long-read strain-level metagenomic classification, enhanced with Metagenome Assembly-Driven Database Reduction.

contig-to-reference mapping reassignment based on an expectation-maximization algorithm for database reduction,

mapping-based tools such as MetaMaps [24], PathoScope2 [25], EMU [26] and MORA [27], which rely on read alignments and reassignment algorithms, offer higher precision at a greater computational cost.

Kraken2, perform well at the species level

The implementation of the EM algorithm in MADRe is inspired by PathoScope2 [46] and EMU [26].

range of metagenomic classification tools have been developed, which can be broadly categorized into marker-based, DNA-to-protein and DNA-to-DNA approaches, as described in [4].

K-mer-based tools such as Kraken2 [14], KrakenUniq [15], Bracken [16], Centrifuge [17], CLARK/CLARKS [18, 19], Ganon [20, 21], Taxor [22], and Sylph [23] are known for their speed and scalability to large databases, but often trade precision for speed

MADRe achieves high precision and strain-level resolution while maintaining lower memory usage and runtime compared to existing tools

assembly tools remain prone to large-scale errors caused by repeats in the genome, leading to inaccurate detection of AMR gene content

we present Amira, a tool to detect AMR genes directly from unassembled long-read sequencing data

the fact that multiple consecutive genes lie within a single read to construct gene-space de Bruijn graphs where the k-mer alphabet is the set of genes in the pan-genome of the species under study

reads corresponding to different copies of AMR genes can be effectively separated based on the genomic context of the AMR genes, and used to infer the nucleotide sequence of each copy

compare the number of fully (>90%) present genes with good read support by Amira and Flye with AMRFinderPlus

quantifying the improvement in recall when handling heterogeneous data.

We present Autocycler, a command-line tool for generating accurate bacterial genome assemblies by combining multiple alternative long-read assemblies of the same genome

Autocycler builds a compacted De Bruijn graph from the input assemblies, clusters and filters contigs, trims overlaps and resolves consensus sequences by selecting the most common variant at each locus

“module scripts” (or “modules” for short), which are Nextflow scripts that can be “included” by other scripts

help you organize a large pipeline into multiple smaller files and take advantage of modules created by others

To migrate this code to DSL2, you need to move all of your channel logic throughout the script into a workflow definition

initial goal of tinybio was to remove the barrier to entry for running bioinformatics packages

Scientists spend a significant proportion of their time transforming and structuring data for analysis

driving the development of community-centric tools on Seqera.io, empowering scientists worldwide to leverage modern software capabilities on demand

removing barriers to entry to bioinformatics

steep learning curve that prevents newcomers from getting started fast

meet scientists at every stage of their work

Suggesting

Generating Nextflow code

Asking bioinformatics questions

contextually relevant answers

Beyond just a chat interface

ability to test their code in the interface

Programmed with a deep understanding of Nextflow, common bioinformatics tools, and the overarching scientific community.

extensive testing with scientists

able to identify the root cause of errors, help troubleshoot, and suggest edits

has deep knowledge of the errors

ability to pair with bioinformatics test data and generate local test scripts

generate and run unit tests.

not only give you the initial conversion, but also run the stages of the code that it generates with sample data and iteratively correct any code that yields runtime errors

convert a pipeline from Bash/CWL/WDL to Nextflow

AI can be a powerful tool for helping scientists dig into results and more quickly identify interesting patterns

key to figure out how we can get the right context on your pipeline results

Seqera AI – a bioinformatics agent purpose-built for the scientific lifecycle

native integration with MultiQC where you can enable automatic, in-line analysis of MultiQC reports

fully extensible endpoint in Seqera AI, so that any bioinformatics tool can build their own AI integration.

threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated

importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering

increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines

we focus specifically on concerns that lie at the interface of biological data and computational inference with the goal of inspiring increased research and educational activities in this space

how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery

it supports both short and long reads

When given well-crafted instructions, these chatbots hold the potential to significantly augment bioinformatics education and research

Crafting effective prompts can be challenging

role prompting that assigns a role to the chatbot, few-shot prompting that provides relevant examples, and chatbot self-reflection that improves responses based on task feedbacks

domain-specific knowledge.

In addition, varying study designs will require project-specific statistical analyses.

Hecatomb’s design philosophy recognizes that there are no “perfect” databases or search algorithms

Instead, Hecatomb relies on providing a compiled and rich set of data for search result evaluation

Hecatomb and Conda handle the installation of all dependencies

use of isolated Conda environments for Hecatomb minimizes package version conflicts, minimizes overhead when rebuilding environments for updated dependencies, and allows maintenance and customization of different Hecatomb versions.

While Hecatomb is a Snakemake pipeline, it uses the Snaketool command line interface to make running the pipeline as simple as possible [95]. Snaketool populates required file paths and configuration files, allowing Hecatomb to be configured and run with a simple command

An opt-in feature for now, strict syntax enables consistent behavior between the Nextflow CLI and language server, and enables numerous new features

more actionable error messages

output on the terminal highlighting exactly where the problem lies

This new specification enables more specific error reporting, ensures more consistent code, and will allow the Nextflow language to evolve independently of Groovy.

strict syntax will eventually become the only way to write Nextflow code, and new language features will be implemented only in the strict syntax

prepare for the strict syntax

assignments are allowed only as statements:

use higher-order functions, such as the each method, instead:

use if-else statements

environment variables

Use a multi-line string

Any Groovy code can be moved into the lib directory, which supports the full Groovy language.

For Groovy code that is complicated or if it depends on third-party libraries, it may be better to create a plugin

local executor is very useful for workflow development and testing purposes

Nextflow provides an abstraction between the workflow’s functional logic and the underlying execution system (or runtime)

its cost-effectiveness and lower data requirements compared to metagenomic whole-genome sequencing (WGS)

interactive analysis applications

namely Jupyter, RStudio, VS Code, and Xpra).

You should always be suspicious of your metabolic reconstructions, and particularly when you are using short reads where you have partial matches.

Nextflow, a workflow management system that uses Docker technology for the multi-scale handling of containerized computation

found that multi-scale containerization, which makes it possible to bundle entire pipelines, subcomponents and individual tools into their own containers, is essential for numerical stability

The dataflow model is superior to alternative solutions based on a Make-like approach, such as Snakemake16, in which computation involves the pre-estimation of all computational dependencies, starting from the expected results up until the input raw data

requires a directed acyclic graph (DAG), whose storage requirement is a limiting factor for very large computations.

the top to bottom processing model used by Nextflow follows the natural flow of data analysis, it does not require a DAG

Although the graphical user interface (GUI) in Galaxy offers powerful support for de novo pipeline implementation by non-specialists, it also imposes a heavy development burden because any existing and validated third-party pipeline must be re-implemented and re-parameterized using the GUI.

analysis pipeline for assembly, binning and annotation of metagenomes.

we present Emu, an approach that uses an expectation–maximization algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads.

Chat sessions100 per month

It is a good idea to specify the pipeline version when running the pipeline on your data.

If you are the only person to be running this pipeline, you can create a local config file and use this.

Configuration parameters are loaded one after another and overwrite previous values. Hardcoded pipeline defaults are first, then the user’s home directory, then the work directory, then every -c file in the order supplied, and finally command line --<parameter> options.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file. Pipeline settings can be provided in a yaml or json file via -params-file <file>.

Differential abundance analysis for relative abundance from microbial community analysis are plagued by multiple issues that aren’t fully solved yet. But some approaches seem promising

Profiles can give configuration presets for different compute environments.

Furthermore, we present an update on NanoPlot and NanoComp from the NanoPack tools (De Coster et al. 2018).

Improvements to NanoPlot and NanoComp are, among code optimizations, the generation of additional plots, using dynamic HTML plots from the Plotly library, and enabling further exploration by the end users

Chopper is a tool that combines the utility of NanoFilt and NanoLyse, for filtering sequencing reads based on quality, length, and contaminating sequences, delivers a 7-fold speed up compared to the Python implementation, making use of the Rust-Bio library

Call for papers - Application of large language models in genome analysis

Submission Deadline: 28 November 2025

For Nextflow DSL2 nf-core pipelines - parameters defined in the parameter block in custom.config files WILL NOT override defaults in nextflow.config! Please use -params-file in yaml or json format in these cases:

Please only use Conda as a last resort, i.e., when it’s not possible to run the pipeline with Docker or Singularity.

Magnet is a whole-genome read-mapping-based method that provides detailed presence and absence calls for bacterial genomes

Lemur is a marker-gene-based method

methods explicitly designed for long reads tend to perform better.

experimental evaluation focused primarily on precision and recall

important to evaluate scalability and fitness for execution in low-resource environments such as laptops and tablet computers

long-read technologies offer potential for portable and streaming sequence analysis

Both methods require a FASTQ file containing sequencing reads as input.

Several new tools have recently been developed to leverage long-reads for taxonomic profiling

Lemur additionally requires a marker gene (MG) database, whereas Magnet requires a (ideally small) set of genomes

Our results indicate that Lemur can efficiently process large datasets within minutes to hours in limited computational resource settings.

can improve precision by detecting and filtering out many false positive calls

Lemur and Magnet have limitations that vary by use case. Reliance on bacterial marker genes necessarily implies it cannot generalize to viral genome classification

reliance on the marker genes makes it less sensitive than alternatives like Kraken 2 or MetaMaps, which use all long reads and complete genomes.

our study focused on taxonomic profiling and binary presence and absence metrics for taxa

The EM algorithm begins by initializing F (t) to the uniform distribution and initializing P (r|t) for each read and taxon pair (r, t).

The goal of Magnet is to detect and remove potential false positives by performing competitive read alignment leveraging all of the reads mapped against the entire reference genome

As input, Magnet requires reads as well as a taxonomic abundance profile (estimated from the input reads e.g. using Lemur)

Lastly, Magnet marks species as present or absent.

Multiple genome alignment, the process of identifying nucleotides across multiple genomes which share a common ancestor

We introduce a partitioning option to Parsnp, which allows the input to be broken up into multiple parallel alignment processes which are then combined into a final alignment

improves our previous method, MHG-Finder, by utilizing a guide tree to significantly improve scalability and provide more informative biological results

Whole-genome alignments play a crucial role in downstream analyses in comparative genomic studies

A maximal homologous group, or MHG, is defined as a maximal set of maximum-length sequences whose evolutionary history is a single tree

processes such as horizontal gene transfer or gene duplication and loss may disrupt this homology by recombining only parts of genes, causing gene fission or fusion

Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations

Bacterial genome dynamics

encompassing a single metagenome coassembly graph constructed from all samples in a series

log fold change in graph coverage between subsequent samples is then calculated to call SVs

show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes

innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis

studying SVs across diverse and poorly characterized microbial communities

Recent work utilizes coassembly graphs for metagenomes to decompose strain diversity into haplotypes (30), but to the best of our knowledge, this is the first time coassembly graph patterns have been used for automated detection of SVs in a metagenome series.

In isolate genomics, the goal of SV detection is relatively straightforward: detect long genomic differences between a sequence and reference genome that can be classified as an insertion, deletion, inversion, duplication, translocation, or any combination

You can install modules from nf-core/modules in your pipeline using nf-core modules install. A module installed this way will be installed to the ./modules/nf-core/modules directory.

Nextflow automatically creates and activates the Conda environment(s) given the dependencies specified by each process.

The use of Conda recipes specified using the conda directive needs to be enabled explicitly in the pipeline configuration file (i.e. nextflow.config):

conda.enabled = true

conda.useMicromamba

Uses the micromamba binary instead of conda to create Conda environments

Any channel in the workflow can be assigned to an output, including process and subworkflow outputs. This approach is intended to replace the publishDir directive.

assembly

challenging in complex environmental samples consisting of hundreds to thousands of populations

Mapler is a metagenome assembly and evaluation pipeline

Hi-Fi long read

novel metrics assessing the diversity that remains uncaptured by the assembly process

output: path "${greeting}-output.txt" script: """ echo '$greeting' > '$greeting-output.txt'

You can think of view() as a debugging tool, like a print() statement in Python

We prefer to be explicit to aid code clarity, as such the $it syntax is discouraged and will slowly be phased out of the Nextflow language.

We are using an operator closure here - the curly brackets.

a process will emit value channels if it is invoked with all value channels, including simple values which are implicitly wrapped in a value channel.