From b22be678425d09e8e62a1080995e9dfb497c85e1 Mon Sep 17 00:00:00 2001
From: JeanMainguy <jean.mainguy@outlook.fr>
Date: Thu, 11 Jan 2024 20:46:04 +0100
Subject: [PATCH] add bold in fig caption and adjust svg

---
 paper/binette_overview.svg | 369 ++++++++++++++++++-------------------
 paper/paper.md             |   4 +-
 2 files changed, 182 insertions(+), 191 deletions(-)

diff --git a/paper/binette_overview.svg b/paper/binette_overview.svg
index 43f7ecd..8228c17 100644
--- a/paper/binette_overview.svg
+++ b/paper/binette_overview.svg
@@ -133,10 +133,7 @@
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-               id="tspan32">intermediate<tspan
+           x="26.756784"
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    y="130.76616"
    id="tspan33"> </tspan></tspan><tspan
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-               id="tspan35">Bins are scored<tspan
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-               x="26.756784"
-               y="130.34253"
-               id="tspan40">redundant bins</tspan></text></g></g><text
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diff --git a/paper/paper.md b/paper/paper.md
index 6c824d9..8fabfab 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -38,7 +38,9 @@ We present Binette, a bin refinement tool inspired by metaWRAP's bin refinement
 Binette is a Python reimplementation and enhanced  version of the bin refinement module used in metaWRAP. It takes as input sets of bins generated by various binning tools. Using these input bin sets, Binette constructs new hybrid bins using basic set operations. Specifically, a bin can be defined as a set of contigs, and when two or more bins share at least one contig, Binette generates new bins based on their intersection, difference, and union. This approach differs from metaWRAP, which exclusively generates hybrid bins based on bin intersections and allows Binette to expand the range of possible bins.
 
 
-![Overview of Binette Steps. (A) Binette Workflow Overview: Input bins serve as the basis for generating intermediate bins. Each bin undergoes a scoring process utilizing quality metrics provided by CheckM2. Subsequently, the bins are sorted based on their scores, and a selection process is executed to retain non-redundant bins. (B) Intermediate Bin Creation Example: Bins are represented as square shapes, each containing colored lines representing the contigs they contain. Creation of intermediate bins involves the initial bins sharing at least one contig. Set operations are applied to the contigs within the bins to generate these intermediate bins.](./binette_overview.svg)
+![**Overview of Binette Steps**. **(A) Binette Workflow Overview**: Input bins serve as the basis for generating intermediate bins. Each bin undergoes a scoring process utilizing quality metrics provided by CheckM2. Subsequently, the bins are sorted based on their scores, and a selection process is executed to retain non-redundant bins. **(B) Intermediate Bin Creation Example**: Bins are represented as square shapes, each containing colored lines representing the contigs they contain. Creation of intermediate bins involves the initial bins sharing at least one contig. Set operations are applied to the contigs within the bins to generate these intermediate bins.](./binette_overview.svg)
+
+
 
 
 Bin completeness and contamination are assessed using CheckM2 [@chklovski2023checkm2]. Bins are scored using the following scoring function: $completeness - weight * contamination$, with the default weight set to 2. These scored bins are then sorted, facilitating the selection of a final new set of non-redundant bins. The ability to score bins is based on CheckM2 rather than CheckM1 as in the metaWRAP pipeline. CheckM2 uses a novel approach to evaluate bin quality based on machine learning techniques. This approach improves speed and also provides better results than CheckM1. Binette initiates CheckM2 processing by running its initial steps once for all contigs within the input bins. These initial steps involve gene prediction using Prodigal and alignment against the CheckM2 database using Diamond [@buchfink2015diamond]. Binette uses Pyrodigal [@larralde2022pyrodigal], a Python module that provides bindings and an interface to Prodigal [@hyatt2010prodigal]. The intermediate Checkm2 results are then used to assess the quality of individual bins, eliminating redundant calculations and speeding up the refinement process.