Automated image processing from movies to 2D classes. Includes CTF estimation, quality filters, box size estimation, training a data-specific picking model, and three 2D classification jobs (25k, 50k, and 100k particles) using Relion.

Automated image processing from movies to 2D classes. Includes CTF estimation, quality filters, box size estimation, training a data-specific picking model, and three 2D classification jobs (25k, 50k, and 100k particles) using CryoSPARC.

Adapting to Tilted Samples

To process tilted experiments, use ”tilted” templates. These are modified workflows tailored to handle high drift and lower resolution commonly observed in tilted samples. Key adjustments include:

• Motion Correction Filters: ...

Automated image processing from movies to 2D classes. Includes CTF estimation, quality filters, box size estimation, training a data-specific picking model, and three 2D classification jobs (25k, 50k, and 100k particles) using CryoSPARC.

Image processing from movies to micrographs, including CTF estimation and quality filters for image curation.

Adapting to Tilted Samples

To process tilted experiments, use ”tilted” templates. These are modified workflows tailored to handle high drift and lower resolution commonly observed in tilted samples. Key adjustments include:

• Motion Correction Filters: Max per-frame shift = 30; Global drift = 120.

• CTF Consensus Filters: Adapted to 6.5 Å and 8.5 Å resolution cutoffs.

• **Defocus ...

Image processing pipeline from movies to micrographs, including CTF estimation and quality filters for image curation.

A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support

Introduction

Knowledge graphs and large language models (LLMs) serve as key tools for biomedical knowledge integration and reasoning, facilitating the structured organization of literature and the discovery of deep semantic relationships. However, existing methods still face challenges in knowledge mining and cross-document reasoning: knowledge graph construction is constrained ...

A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support

Introduction

Knowledge graphs and large language models (LLMs) serve as key tools for biomedical knowledge integration and reasoning, facilitating the structured organization of literature and the discovery of deep semantic relationships. However, existing methods still face challenges in knowledge mining and cross-document reasoning: knowledge graph construction is constrained ...

SINGLE-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations.

IMPORTANT:

• For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling).
• SELECT the mot ADAPTED VADR MODEL for annotation (see vadr parameters).

PAIRED-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations.

IMPORTANT:

• For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling).
• SELECT THE MOST ADAPTED VADR MODEL for annotation (see vadr parameters).

DeepAnnotation can be used to perform genomic selection (GS), which is a promising breeding strategy for agricultural breeding. DeepAnnotation predicts phenotypes from comprehensive multi-omics functional annotations with interpretable deep learning framework. The effectiveness of DeepAnnotation has been demonstrated in predicting three pork production traits (lean meat percentage at 100 kg [LMP], loin muscle depth at 100 kg [LMD], back fat thickness at 100 kg [BF]) on a population of 1940 Duroc ...