Malaria vector genome observatory


Explore the unknowns in Anopheles mosquito biology and control.

The observatory is a collaborative effort to sequence the genomes of thousands of Anopheles mosquitoes collected in countries affected by malaria, and use those data to help answer some of the big questions in our path towards malaria elimination.

Find Out More

What do we need to know?

Despite more than a century of research, we still have much to learn about the mosquitoes that transmit malaria.


(1) How is malaria is being transmitted?

We need a better picture of which Anopheles mosquito species are transmitting malaria in different places, and we need to fully understand the biology of those species. We also need to know how this is changing in response to interventions, urbanisation and climate change.


(2) Where and how is insecticide resistance evolving and spreading?

Insecticide resistance is the biggest threat to our current front line tools for mosquito control. When resistance emerges, we need to identify and characterise it quickly, assess the risk, and track it as it spreads.


(3) How can we move beyond control towards elimination?

Our current tools for mosquito control can only take us so far. We need to invent new ways to stop mosquitoes transmitting malaria, and be smart in how we use them.


By sequencing mosquitoes and making the data available for surveillance, research and education, the observatory provides an opportunity to shed new light on these questions.

Get Started

The observatory at a glance


Data

Access carefully-curated data which has been optimised for analysis in the cloud.

Training

Learn how to analyse data in the observatory through our hands-on online training course.

API

Use our open source Python package to explore and visualise data interactively.

Compute

Access free cloud computing services to analyse data wherever you are.

Partners

Scientists and engineers from around the globe are contributing samples, data and expertise to help build the observatory.

Research

Data from the observatory are powering new research into vector biology and control.

Data

Access data from whole-genome sequencing of Anopheles mosquitoes collected from natural populations in countries affected by malaria.


Anopheles gambiae complex

15,036 genomes from 25 countries

View Data Docs

Anopheles funestus subgroup

2,721 genomes from 15 countries

View Data Docs


Anopheles minimus

302 genomes from 1 country

View Data Docs

Training

Learn about the biology, technology, data and analytical methods involved in genomic surveillance of Anopheles mosquitoes.



Video lectures in French or English and notebooks with executable code examples.

Study at your own pace, or enroll in an online course to learn alongside others with support from experienced teaching assistants.

API

Built for exploratory population genomics, our cloud-native analytical software for Python supports a range of data access and visualisation functions.


Explore available samples by country of origin, collection date and taxon.
Explore available samples by collection location.
Compute frequencies of genetic variants in genes of interest.
Compare the frequency of genetic variants between different geographical areas.
Analyse changes in variant frequencies over time.
Browse sequence read alignments and assess the evidence for genetic variation in individual samples.
Visualise sequence read coverage and evidence for copy number variation in individual samples.
Visualize and compare copy number variation in multiple samples.
Explore population structure with principal components analysis.
Investigate hybridisation and introgression between species with ancestry-informative markers.
Quantify and compare genetic diversity between populations.
Infer runs of homozygosity and investigate evidence for inbreeding.
Perform genome-wide selection scans to discover new resistance genes.
Find genes where adaptive gene flow is occurring between countries or species.
Use haplotype clustering to investigate selective sweeps and gene flow within a gene of interest.
Use haplotype networks to explore selective sweeps and gene flow within a gene of interest.

Compute

Run your analysis in the cloud using these computing services.


Google Colab

Colab is an interactive computing service provided for free by Google Research and is ideal for exploratory analyses.


MalariaGEN Datalab

The Genomic Surveillance Unit at the Sanger Institute hosts a JupyterHub service running in Google Cloud that is available for free to observatory partners for more intensive analyses.


Terra

Terra is a cloud platform for biomedical research supporting workflows and interactive notebooks, and can be used for a wide range of analyses.


Alternatively, observatory data can be downloaded to your own compute resources to run analyses locally.

Partners

Groups and individuals from around the world have contributed biological samples, sequencing capacity, computational resources, time and expertise to build the observatory.

The observatory is the result of a concerted effort by multiple projects and initiatives.


The Pan-African Mosquito Control Association (PAMCA) Anopheles Genomics programme is working to build capacity for malaria vector genomics across Africa.


The Genomics for African Anopheles Resistance (GAARD) project aims to identify new genes and regulatory regions associated with insecticide resistance.


Target Malaria is developing innovative approaches to reduce the population of malaria-transmitting mosquitoes in sub-Saharan Africa.


PAMGEN

The PAMGEN project is researching genetic interactions between malaria parasites, vectors and human communities across different African environments.


The Malaria Genomic Epidemiology Network (MalariaGEN) aims to create genetic data resources to support the control and elimination of malaria.


Research

Data from the observatory are powering new and ongoing research into the biology and control of malaria vectors.


Population genomics reveal distinct and diverging populations of An. minimus in Cambodia

St. Laurent et al. (2022) Nature Communications Biology.

diploS/HIC: An Updated Approach to Classifying Selective Sweeps

Kern and Schrider (2018) G3 Genes|Genomes|Genetics.

How it works