Li Rui - Shanghaitech University

Example Talk

Sat, 01 Jun 2030 13:00:00 +0000

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TRACE

Tue, 18 Aug 2026 00:00:00 +0000

TRACE is an integrated experimental–computational framework for untargeted LC–MS metabolomics. It uses complete ¹³C/¹⁵N labeling to validate biological metabolites, then maps how competing unlabeled nutrients enter metabolism by isotopic dilution rather than label enrichment.

The work is published in Analytical Chemistry (doi:10.1021/acs.analchem.6c02292). Software documentation is at drruili.github.io/TRACE. TRACE runs on MSdev objects and xcms peak tables.

Why TRACE

Untargeted LC–MS can survey microbial metabolism at scale, but two problems remain tightly coupled:

Which peaks are biological? Modern instruments report tens of thousands of features. Many are adducts, in-source fragments, or chemical contaminants rather than metabolites made by the cell.
Which nutrients are used when several are present? Classical isotope tracing follows one labeled substrate at a time. It does not ask how cells choose among competing carbon and nitrogen sources.

Existing tools, including our earlier single-peak method PAVE, validate features in isolation. Fixed m/z and retention-time tolerances then generate large numbers of false connections as sensitivity increases, and they struggle to group the many MS forms of one metabolite.

TRACE addresses both problems with one design: fully label cells with ¹³C-glucose and ¹⁵N-ammonium, treat isotope-confirmed peaks as network seeds, calibrate error tolerances from those seeds, then map unlabeled nutrient fate by isotopic dilution.

Experimental design

Saccharomyces cerevisiae CEN.PK was grown for more than ten generations in minimal medium with [U-¹³C₆]-glucose and [U-¹⁵N₂]-(NH₄)₂SO₄ as the sole carbon and nitrogen sources, so de novo metabolites carry predictable CN mass shifts.

Four tracer groups define the seeds:

Sample	Label	Role
Unlabeled	C0N0	Light carbon and nitrogen
¹⁵N-ammonium	C0Ny	Light carbon, heavy nitrogen
¹³C-glucose	CxN0	Heavy carbon, light nitrogen
Dual label	CxNy	Heavy carbon and nitrogen

For nutrient competition, unlabeled supplements (leucine, threonine, tryptophan, adenine, uracil, acetate, or a 14-compound mixture) were added on this labeled background. Incorporation of an unlabeled nutrient dilutes the ¹³C/¹⁵N labeling of downstream metabolites.

Figure 1. TRACE workflow. Isotope-confirmed peaks seed a network of adduct, fragment, and isotope connections; dynamic m/z and RT tolerances are learned from true CN links.

From seeds to a calibrated network

TRACE first keeps coeluting peak pairs whose m/z differences match theoretical ¹³C/¹⁵N shifts, then requires a complete unlabeled / ¹⁵N / ¹³C / dual-label CN bundle whose intensities match the expected 1:1:1:1 pattern (Pearson ρ > 0.75).

In a representative positive-mode data set this collapsed ~25,000 features and hundreds of millions of pairs to 1,098 high-confidence bundles. True CN links cluster tightly around zero m/z error and RT shift. TRACE fits that cluster as a Gaussian signal against a nonparametric background and uses the central 99% of the signal as dynamic tolerances, then propagates adduct, fragment, and isotope edges only inside those bounds.

Figure 2. Network construction and dynamic filtering. High-ρ CN bundles define data-driven m/z and RT tolerances that suppress implausible isotopes and rare fragment forms.

Global assignment and annotation

Edges are typed as isotope, fragment, or adduct and scored as chemical transformations (for example [M+H]⁺ → [M+Na]⁺ is H₋₁Na₁). TRACE then partitions seeds into compatible subnetworks so that one metabolite and its MS forms share a seed, instead of resolving conflicting pairwise assignments.

Against an authentic-standard library, TRACE removed PAVE hits with large m/z or RT error, corrected adduct mis-assignments caused by pairwise conflicts, and recovered features that PAVE had missed. About 70% of TRACE-annotated peaks matched HMDB, YMDB, KEGG, or an in-house database; about 27% of candidates matched in-house RT standards.

Figure 3. Network assignment. TRACE groups adducts, fragments, and isotopologues of one metabolite and annotates a representative seed.

More sensitivity is not more biology

The same labeled yeast extract was analyzed on four Orbitrap platforms (Q Exactive Plus, Exploris 480, Astral, Excedion Pro) and at several resolutions. Total features rose by 331% from QE Plus to Excedion Pro, while TRACE-validated metabolites rose only 117%. The share of high-confidence biological annotations fell from 2.94% to 1.48%. Extra peaks are mostly non-biological unless annotation is specific.

Figure 4. Instrument comparison. Feature count grows faster than TRACE-validated metabolites as sensitivity and resolution increase.

Nutrient fate under competition

With formulas and CN networks fixed from the four tracer groups, TRACE maps nutrient conditions onto that network. For each metabolite, the labeling fraction is the abundance-weighted ¹³C or ¹⁵N atom count; the absorption fraction is the drop in labeling relative to the fully labeled control.

The complete mixture contributed carbon and nitrogen across sulfur, purine, and amino-acid metabolism. Individual supplements were pathway-selective: tryptophan into tryptophan metabolism, adenine into purine metabolism, threonine into serine/glycine metabolism, acetate as C₂/C₄/C₆ units.

Two molecules test the method at opposite extremes of pathway architecture:

Glutathione (GSH) is a linear tripeptide. Its isotopologues tracked glutamate, glycine, and cysteine, including acetate-derived carbon in the glutamate moiety and threonine-derived C₂N₁ in glycine.
NAD⁺ is a hub (adenine + two riboses + nicotinamide). Unlabeled adenine produced both salvage (C₅N₀) and ongoing de novo (C₁₀N₅) forms; unlabeled tryptophan diluted nicotinamide via kynurenine; a C₀N₁ nicotinamide form indicated glutamine nitrogen entering independently of the carbon backbone.

Figure 5. Nutrient-source attribution. Isotopic dilution maps competing unlabeled nutrients into pathways; GSH and NAD⁺ illustrate linear assembly versus convergent biosynthesis.

Software

A typical analysis loads a pre-processed MSdev object (xcms peaks with sample.type set to the four isotope groups), runs the pipeline, and exports tables:

library(MSdev)
library(TRACE)

obj <- TRACE_workflow(
 obj,
 rt.tol = 10,
 ppm = 5,
 cpdb = "path/to/trace.cp.db.xlsx"
)

TRACE_export(obj, file = "TRACE_results.xlsx")

Version	Role
`v1.0.0`	Code used for the published article
`main` (1.1.0+)	Ongoing development

Install the article snapshot with devtools::install_github("DrRuiLi/TRACE", ref = "v1.0.0"). See Get started for the full workflow.

Scope

TRACE is built for systems in which complete isotopic labeling (the 1:1:1:1 CN bundle) is achievable, as in yeast grown for many generations on labeled glucose and ammonium. It is not a drop-in seed detector for mammalian cells. High-confidence TRACE annotations can still be transferred by running mammalian samples in the same batch as fully labeled microbial references and aligning features.

TRACE: An Integrated Isotope-Tracing Framework for Metabolite Validation and Nutrient Fate Mapping

Tue, 18 Aug 2026 00:00:00 +0000

TRACE is an integrated experimental and computational framework for untargeted LC–MS metabolomics. Fully labeled ¹³C-glucose / ¹⁵N-ammonium cultures supply high-confidence CN bundles that seed a peak–peak network; dynamic m/z and retention-time tolerances are learned from those seeds, then unlabeled nutrients are mapped by isotopic dilution.

The accompanying R package is TRACE (GitHub: DrRuiLi/TRACE). A project-level overview, including workflow figures, is on the TRACE project page.

Why a new framework

Untargeted LC–MS surveys microbial metabolism at scale, but two problems remain coupled: many detected peaks are adducts, fragments, or contaminants rather than cell-made metabolites, and classical tracing follows one labeled substrate at a time. TRACE treats isotope-confirmed peaks as network seeds, calibrates error tolerances from those seeds, and asks how cells partition competing carbon and nitrogen sources.

Main results

Calibrated networks. Complete unlabeled / ¹⁵N / ¹³C / dual-label CN bundles (Pearson ρ > 0.75) collapsed ~25,000 features to 1,098 high-confidence seeds in a representative positive-mode data set, from which data-driven m/z and RT tolerances are learned.
Better assignment than pairwise methods. Global network assignment grouped adducts, fragments, and isotopologues of one metabolite, removed large m/z/RT errors seen in PAVE, and recovered missed features. About 70% of TRACE-annotated peaks matched HMDB, YMDB, KEGG, or an in-house database.
Sensitivity is not biology. From Q Exactive Plus to Excedion Pro, total features rose 331% while TRACE-validated metabolites rose 117%, and the share of high-confidence annotations fell from 2.94% to 1.48%.
Nutrient fate under competition. Unlabeled leucine, threonine, tryptophan, adenine, uracil, acetate, or a 14-compound mixture diluted the labeled background in a pathway-selective way. Glutathione tracked linear amino-acid assembly; NAD⁺ revealed concurrent salvage, de novo, and independent nitrogen entry.

Software versions

Version	Role
`v1.0.0`	Code used for this article
`main` (1.1.0+)	Ongoing development

devtools::install_github("DrRuiLi/TRACE", ref = "v1.0.0")

Mass Spectrometry based Isotopomer Parsing

Mon, 17 Mar 2025 00:00:00 +0000

Gout Biomarkers

Sun, 16 Mar 2025 00:00:00 +0000

To discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and establish a predictive model by machine learning (ML) algorithms.

Serum samples from a discovery cohort with 163 InGF and 239 FrGF patients were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.

Differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism.

Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF.

Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.

In summary, Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

Blog with Jupyter Notebooks!

Sat, 04 Nov 2023 00:00:00 +0000

from IPython.core.display import Image
Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')

print("Welcome to Academic!")

Welcome to Academic!

Organize your notebooks

Place the notebooks that you would like to publish in a notebooks folder at the root of your website.

Import the notebooks into your site

pipx install academic
academic import 'notebooks/**.ipynb' content/post/ --verbose

The notebooks will be published to the folder you specify above. In this case, they will be published to your content/post/ folder.

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent versus Infrequent Gout Flares

Wed, 01 Nov 2023 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

Metabolomics in Hyperuricemia and Gout

Tue, 21 Mar 2023 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

Plasma Metabolomics Reveals Systemic Metabolic Alterations of Subclinical and Clinical Hypothyroidism

Sun, 01 Jan 2023 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

Welcome to Hugo Blox Builder, the website builder for Hugo

Sun, 13 Dec 2020 00:00:00 +0000

import libr
print('hello')

Overview

The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
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Hugo Academic CLI: Automatically import publications from BibTeX

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Hello R Markdown

Tue, 01 Dec 2020 21:13:14 -0500

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

You can embed an R code chunk like this:

summary(cars)
## speed dist 
## Min. : 4.0 Min. : 2.00 
## 1st Qu.:12.0 1st Qu.: 26.00 
## Median :15.0 Median : 36.00 
## Mean :15.4 Mean : 42.98 
## 3rd Qu.:19.0 3rd Qu.: 56.00 
## Max. :25.0 Max. :120.00
fit <- lm(dist ~ speed, data = cars)
fit
## 
## Call:
## lm(formula = dist ~ speed, data = cars)
## 
## Coefficients:
## (Intercept) speed 
## -17.579 3.932

Including Plots

You can also embed plots. See Figure 1 for example:

par(mar = c(0, 1, 0, 1))
pie(
 c(280, 60, 20),
 c('Sky', 'Sunny side of pyramid', 'Shady side of pyramid'),
 col = c('#0292D8', '#F7EA39', '#C4B632'),
 init.angle = -50, border = NA
)

Figure 1: A fancy pie chart.

Writing technical content in Markdown

Fri, 12 Jul 2019 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.

Highlight your code snippets, take notes on math classes, and draw diagrams from textual representation.

On this page, you’ll find some examples of the types of technical content that can be rendered with Wowchemy.

Examples

Code

Wowchemy supports a Markdown extension for highlighting code syntax. You can customize the styles under the syntax_highlighter option in your config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Mindmaps

Wowchemy supports a Markdown extension for mindmaps.

Simply insert a Markdown markmap code block and optionally set the height of the mindmap as shown in the example below.

A simple mindmap defined as a Markdown list:


```markmap {height="200px"}
- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

A more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Wowchemy Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Wowchemy Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Charts

Wowchemy supports the popular Plotly format for interactive charts.

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Math

Wowchemy supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Diagrams

Wowchemy supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

Tables

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

Callouts

Academic supports a shortcode for callouts, also referred to as asides, hints, or alerts. By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Spoilers

Add a spoiler to a page to reveal text, such as an answer to a question, after a button is clicked.

{{< spoiler text="Click to view the spoiler" >}}
You found me!
{{< /spoiler >}}

renders as

Click to view the spoiler

You found me!

Icons

Academic enables you to use a wide range of icons from Font Awesome and Academicons in addition to emojis.

Here are some examples using the icon shortcode to render icons:

{{< icon name="terminal" pack="fas" >}} Terminal
{{< icon name="python" pack="fab" >}} Python
{{< icon name="r-project" pack="fab" >}} R

renders as

Terminal
Python
R

Slides

Tue, 05 Feb 2019 00:00:00 +0000

Create slides in Markdown with Hugo Blox Builder

Hugo Blox Builder | Documentation

Features

Efficiently write slides in Markdown
3-in-1: Create, Present, and Publish your slides
Supports speaker notes
Mobile friendly slides

Controls

Next: Right Arrow or Space
Previous: Left Arrow
Start: Home
Finish: End
Overview: Esc
Speaker notes: S
Fullscreen: F
Zoom: Alt + Click
PDF Export

Code Highlighting

Inline code: variable

Code block:

porridge = "blueberry"
if porridge == "blueberry":
 print("Eating...")

Math

In-line math: $x + y = z$

Block math:

$$ f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}} $$

Fragments

Make content appear incrementally

{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}

Press Space to play!

One Two Three

A fragment can accept two optional parameters:

class: use a custom style (requires definition in custom CSS)
weight: sets the order in which a fragment appears

Speaker Notes

Add speaker notes to your presentation

{{% speaker_note %}}

- Only the speaker can read these notes
- Press `S` key to view
 {{% /speaker_note %}}

Press the S key to view the speaker notes!

Themes

black: Black background, white text, blue links (default)
white: White background, black text, blue links
league: Gray background, white text, blue links
beige: Beige background, dark text, brown links
sky: Blue background, thin dark text, blue links

night: Black background, thick white text, orange links
serif: Cappuccino background, gray text, brown links
simple: White background, black text, blue links
solarized: Cream-colored background, dark green text, blue links

Custom Slide

Customize the slide style and background

{{< slide background-image="/media/boards.jpg" >}}
{{< slide background-color="#0000FF" >}}
{{< slide class="my-style" >}}

Custom CSS Example

Let’s make headers navy colored.

Create assets/css/reveal_custom.css with:

.reveal section h1,
.reveal section h2,
.reveal section h3 {
 color: navy;
}

Questions?

Ask

Documentation

MSdev

Wed, 27 Apr 2016 00:00:00 +0000

The lack of a universal analytical platform is one of the key factors limiting the biological application and development of metabolomics methodologies. We developed a metabolomics data analysis and method development platform, MSdev. MSdev integrates and develops various core tools and databases, including mass spectrometry data analysis, chemical computations, metabolomics databases, and molecular graph structures. This platform not only facilitates our routine metabolomics and metabolic flux analysis but also supports the development of advanced methods such as MSIP and MFNA.

Li Rui - Shanghaitech University

Example Talk

TRACE

Why TRACE

Experimental design

From seeds to a calibrated network

Global assignment and annotation

More sensitivity is not more biology

Nutrient fate under competition

Software

Scope

TRACE: An Integrated Isotope-Tracing Framework for Metabolite Validation and Nutrient Fate Mapping

Why a new framework

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Mass Spectrometry based Isotopomer Parsing

Gout Biomarkers

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Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent versus Infrequent Gout Flares

Metabolomics in Hyperuricemia and Gout

Plasma Metabolomics Reveals Systemic Metabolic Alterations of Subclinical and Clinical Hypothyroidism

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